Modelling the Riskiness in Country Risk Ratings: An Empirical Analysis of the Trends and Volatilities in Country Risk Ratings and Risk Returns

MODELLING THE RISKINESS IN COUNTRY RISK RATINGS CONTRIBUTIONS TO ECONOMIC ANALYSIS 273 Honory Editors: D. W. JORGENSO...

Author: Suhejla Hoti | Michael McAleer

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MODELLING THE RISKINESS IN COUNTRY RISK RATINGS

CONTRIBUTIONS TO ECONOMIC ANALYSIS 273

Honory Editors: D. W. JORGENSON J. TINBERGEN †

Editors: B. BALTAGI E. SADKA D. E. WILDASIN

Amsterdam – Boston – Heidelberg – London – New York – Oxford Paris – San Diego – San Francisco – Singapore – Sydney – Tokyo

MODELLING THE RISKINESS IN COUNTRY RISK RATINGS

Suhejla Hoti University of Western Australia, Australia Michael McAleer University of Western Australia, Australia

2005 Amsterdam – Boston – Heidelberg – London – New York – Oxford Paris – San Diego – San Francisco – Singapore – Sydney – Tokyo

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q 2005 Elsevier B.V. All rights reserved. This work is protected under copyright by Elsevier B.V., and the following terms and conditions apply to its use: Photocopying Single photocopies of single chapters may be made for personal use as allowed by national copyright laws. Permission of the Publisher and payment of a fee is required for all other photocopying, including multiple or systematic copying, copying for advertising or promotional purposes, resale, and all forms of document delivery. Special rates are available for educational institutions that wish to make photocopies for non-profit educational classroom use. Permissions may be sought directly from Elsevier’s Rights Department in Oxford, UK: phone (+44) 1865 843830, fax (+44) 1865 853333, e-mail: [email protected]. Requests may also be completed on-line via the Elsevier homepage (http://www.elsevier.com/locate/permissions). In the USA, users may clear permissions and make payments through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA; phone (+1) (978) 7508400, fax: (+1) (978) 7504744, and in the UK through the Copyright Licensing Agency Rapid Clearance Service (CLARCS), 90 Tottenham Court Road, London W1P 0LP, UK; phone: (+44) 20 7631 5555; fax: (+44) 20 7631 5500. Other countries may have a local reprographic rights agency for payments. Derivative Works Tables of contents may be reproduced for internal circulation, but permission of the Publisher is required for external resale or distribution of such material. Permission of the Publisher is required for all other derivative works, including compilations and translations. Electronic Storage or Usage Permission of the Publisher is required to store or use electronically any material contained in this work, including any chapter or part of a chapter. Except as outlined above, no part of this work may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission of the Publisher. Address permissions requests to: Elsevier’s Rights Department, at the fax and e-mail addresses noted above. Notice No responsibility is assumed by the Publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made. First edition 2005 British Library Cataloguing in Publication Data

ISBN: 0-444-51837-1 ISSN: 0573-8555 (Series) The paper used in this publication meets the requirements of ANSI/NISO Z39.48-1992 (Permanence of Paper). Printed in the United Kingdom.

To my parents, Nane¨s dhe Babe¨s (SH) To my mother and memory of my father (MM)

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CONTENTS Acknowledgements List of Tables List of Figures

xi xiii xvii

Chapter 1 Introduction

1

1.1 Country risk 1.2 Country risk literature 1.3 Risk ratings and rating systems 1.4 Risk ratings and risk returns for 120 representative countries 1.5 Conditional volatility models for risk ratings and risk returns 1.6 Empirical results 1.7 Conclusion References

1 3 4 5 6 6 7 8

Chapter 2 Country Risk Models: An Empirical Critique

9

2.1 Introduction 2.2 Classification of the data 2.3 Theoretical and empirical model specifications 2.4 Empirical findings 2.5 Conclusion References Appendix 2.1 Description of models Appendix 2.2 Analysis of models

9 11 15 25 28 28 32 70

Chapter 3 Rating Risk Rating Systems

93

3.1 Introduction 3.2 Risk rating industry 3.3 Comparison of country risk rating methodologies 3.4 ICRG country risk ratings 3.5 Conclusion References

93 94 96 106 109 109

viii

Chapter 4 Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 4.1 4.2 4.3

Introduction One-hundred and twenty selected countries Risk ratings, risk returns and volatilities 4.3.1 Central and South Asia 4.3.2 East Asia and the Pacific 4.3.3 East Europe 4.3.4 Middle East and North Africa 4.3.5 North and Central America 4.3.6 South America 4.3.7 Sub-Saharan Africa 4.3.8 West Europe 4.4 Conclusion References Appendix 4.1 ICRG classification of countries by starting date and geographic region

111 111 112 114 115 118 132 140 154 166 174 194 211 212 333

Chapter 5 Conditional Volatility Models for Risk Ratings and Risk Returns

337

5.1 Introduction 5.2 Univariate conditional volatility models 5.3 An asymmetric VARMA – GARCH model 5.4 Conclusion References

337 338 341 345 345

Chapter 6 Univariate and Multivariate Estimates of Symmetric and Asymmetric Conditional Volatilities and Conditional Correlations for Risk Returns

349

6.1 6.2

349 351

Introduction and recommendations for foreign investors Univariate models 6.2.1 Central and South Asia region: GARCH(1,1) and GJR(1,1) estimates 6.2.2 East Asia and the Pacific region: GARCH(1,1) and GJR(1,1) estimates 6.2.3 East Europe region: GARCH(1,1) and GJR(1,1) estimates 6.2.4 Middle East and North Africa region: GARCH(1,1) and GJR(1,1) estimates 6.2.5 North and Central America region: GARCH(1,1) and GJR(1,1) estimates

352 353 354 356 357

ix

6.2.6

South America region: GARCH(1,1) and GJR(1,1) estimates 6.2.7 Sub-Saharan Africa region: GARCH(1,1) and GJR(1,1) estimates 6.2.8 West Europe region: GARCH(1,1) and GJR(1,1) estimates 6.3 Multivariate models: static conditional correlations 6.3.1 Central and South Asia region: static conditional correlation estimates 6.3.2 East Asia and the Pacific region: static conditional correlation estimates 6.3.3 East Europe region: static conditional correlation estimates 6.3.4 Middle East and North Africa region: static conditional correlation estimates 6.3.5 North and Central America region: static conditional correlation estimates 6.3.6 South America region: static conditional correlation estimates 6.3.7 Sub-Saharan Africa region: static conditional correlation estimates 6.3.8 West Europe region: static conditional correlation estimates 6.4 Summary: static conditional correlation estimates 6.5 Conclusion References

359 360 361 363 364 365 366 368 369 370 372 373 375 376 377

Chapter 7 Conclusion

471

7.1 7.2

471 473 473 474 474

7.3

Summary of the monograph Future research 7.2.1 Alternative methods, models and data 7.2.2 New research directions Conclusion

Author Index Country Index Subject Index

475 479 485

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ACKNOWLEDGEMENTS In writing this monograph, we have benefited from the generosity, insightful comments and constructive suggestions of Felix Chan, Clive Granger, Jerry Hausman, Thierry Jeantheau, Christine Lim, Shiqing Ling, Matteo Manera, Colin McKenzie, Les Oxley, Peter Phillips, Dan Slottje and Rob Taylor. Participants at a number of seminars and conferences also provided helpful comments and suggestions. We are grateful to the editor and staff of the International Country Risk Guide for helpful correspondence regarding the risk ratings data. The financial support of the Australian Research Council and the ViceChancellor’s Discretionary Research Fund at the University of Western Australia are gratefully appreciated. Suhejla Hoti Michael McAleer

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LIST OF TABLES 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. 2.8. 2.9. 2.10. 2.11. 2.12. 2.13. 2.14. 2.15. 2.16. 2.17. 2.18. 2.19. 2.20. 2.21. 3.1. 3.2. 3.3. 3.4. 3.5. 3.6. 3.7. 3.8. 3.9. 4.1. 4.2.

Classification by type of data used Classification of pooled data by number of countries Classification of pooled data by number of annual observations Classification of pooled data by number of semi-annual observations Classification of cross-section data by number of countries Classification of cross-section data by number of time series observations Classification by type of model Classification by type of dependent variable used Classification by number of economic and financial explanatory variables Classification by number of political explanatory variables Classification by recognition of omitted explanatory variables Classification by type of omitted explanatory variables Classification by number of proxy variables used Classification by type of proxy variables used Classification by method of estimation Classification by use of diagnostics Types of variables used in debt rescheduling Frequency of types of variables used in debt rescheduling Risk component variables used in country risk ratings Frequency of risk component variables used in country risk ratings Agency data used Risk ratings debut year Classification by the number of countries covered Classification by frequency of ratings Classification by number of ratings compiled Number of risk component variables used Type of risk component variables used Weights assigned to risk component variables (in %) Types of risk rating grades Degree of risk Number of countries by geographic region Number of countries by ICRG starting date

11 12 13 13 14 14 16 18 19 19 20 21 22 22 24 25 26 26 27 27 28 98 98 99 100 102 103 104 105 108 113 114

xiv

6.1. Univariate GARCH(1,1) and GJR(1,1) estimates for Central and South Asia by risk return 6.2. Univariate GARCH(1,1) and GJR(1,1) estimates for East Asia and the Pacific by risk return 6.3. Univariate GARCH(1,1) and GJR(1,1) estimates for East Europe by risk return 6.4. Univariate GARCH(1,1) and GJR(1,1) estimates for Middle East and North Africa by risk return 6.5. Univariate GARCH(1,1) and GJR(1,1) estimates for North and Central America by risk return 6.6. Univariate GARCH(1,1) and GJR(1,1) estimates for South America by risk return 6.7. Univariate GARCH(1,1) and GJR(1,1) estimates for Sub-Saharan Africa by risk return 6.8. Univariate GARCH(1,1) and GJR(1,1) estimates for West Europe by risk return 6.9. Summary of univariate GARCH(1,1) and GJR(1,1) estimates by region 6.10. Preferred model for countries by risk return 6.11. GARCH(1,1) static conditional correlations for Central and South Asia by risk return 6.12. GARCH(1,1) static conditional correlations for East Asia and the Pacific by risk return 6.13. GARCH(1,1) static conditional correlations for East Europe by risk return 6.14. GARCH(1,1) static conditional correlations for Middle East and North Africa by risk return 6.15. GARCH(1,1) static conditional correlations for North and Central America by risk return 6.16. GARCH(1,1) static conditional correlations for South America by risk return 6.17. GARCH(1,1) static conditional correlations for Sub-Saharan Africa by risk return 6.18. GARCH(1,1) static conditional correlations for West Europe by risk return 6.19. Range of conditional correlation coefficients for region by risk return 6.20. Number of conditional correlations for Central and South Asia by range 6.21. Number of conditional correlations for East Asia and the Pacific by range 6.22. Number of conditional correlations for East Europe by range 6.23. Number of conditional correlations for Middle East and North Africa by range

378 380 388 392 400 407 412 424 434 434 438 439 442 443 446 448 450 456 461 462 462 463 464

xv

6.24. Number of conditional correlations for North and Central America by range 6.25. Number of conditional correlations for South America by range 6.26. Number of conditional correlations for Sub-Saharan Africa by range 6.27. Number of conditional correlations for West Europe by range 6.28. Ranking by range of variation in the conditional correlations

465 466 467 468 469

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LIST OF FIGURES 4.1. 4.2. 4.3. 4.4. 4.5. 4.6. 4.7. 4.8. 4.9. 4.10. 4.11. 4.12. 4.13. 4.14. 4.15. 4.16. 4.17. 4.18. 4.19. 4.20. 4.21. 4.22. 4.23. 4.24. 4.25. 4.26. 4.27. 4.28. 4.29. 4.30. 4.31. 4.32. 4.33. 4.34. 4.35. 4.36. 4.37.

Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk

ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings,

risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk

returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns

and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and

volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities

for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for

Bangladesh India Pakistan Sri Lanka Australia Brunei China Hong Kong Indonesia Japan Malaysia Mongolia New Zealand North Korea Papua New Guinea the Philippines Singapore South Korea Taiwan Thailand Vietnam Albania Bulgaria Czech Republic Hungary Poland Romania Russia Slovak Republic Yugoslavia Algeria Bahrain Egypt Iran Iraq Israel Jordan

213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249

xviii

4.38. 4.39. 4.40. 4.41. 4.42. 4.43. 4.44. 4.45. 4.46. 4.47. 4.48. 4.49. 4.50. 4.51. 4.52. 4.53. 4.54. 4.55. 4.56. 4.57. 4.58. 4.59. 4.60. 4.61. 4.62. 4.63. 4.64. 4.65. 4.66. 4.67. 4.68. 4.69. 4.70. 4.71. 4.72. 4.73. 4.74. 4.75. 4.76. 4.77. 4.78. 4.79. 4.80. 4.81. 4.82.

Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk

ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings,

risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk

returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns

and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and

volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities

for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for

Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syria Tunisia the UAE Yemen the Bahamas Canada Costa Rica Cuba Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Trinidad and Tobago the USA Argentina Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela Angola Botswana Burkina Faso Cameroon Congo C^ote d’Ivoire DR Congo Ethiopia Gabon

250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294

xix

4.83. 4.84. 4.85. 4.86. 4.87. 4.88. 4.89. 4.90. 4.91. 4.92. 4.93. 4.94. 4.95. 4.96. 4.97. 4.98. 4.99. 4.100. 4.101. 4.102. 4.103. 4.104. 4.105. 4.106. 4.107. 4.108. 4.109. 4.110. 4.111. 4.112. 4.113. 4.114. 4.115. 4.116. 4.117. 4.118. 4.119. 4.120.

Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk Risk

ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings, ratings,

risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk risk

returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns returns

and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and and

volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities volatilities

for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for for

Ghana Guinea Kenya Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta the Netherlands Norway Portugal Spain Sweden Switzerland Turkey the UK

295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332

CHAPTER 1

Introduction Abstract This monograph presents an econometric analysis of the riskiness in country risk ratings. Country risk and its associated risk ratings are discussed for 120 countries representing eight geographic regions. The rating systems of 10 prominent country risk rating agencies are evaluated. Monthly International Country Risk Guide, country risk ratings and returns are used to estimate and test univariate and multivariate models of volatility for the 120 countries. For the ﬁrst time, estimates of the conditional correlations of risk returns shocks are provided and discussed. Keywords: country risk, country proﬁles, associated ratings, rating systems, agency ratings, conditional volatility models, statistical and econometric criteria, estimation, evaluation JEL classiﬁcations: C22, C51, E44, F34, O16, O57 1.1. Country risk The 1970s witnessed a lending boom by Western banks to Eastern bloc, Latin American, and other less developed countries. This boom was in response to demand for funds by these countries beyond the development support provided by the IMF and the World Bank. Moreover, Western banks needed to recycle their large petrodollar funds from oil producing countries, such as Saudi Arabia and Kuwait. These banks plotted their lending course in pursuit of proﬁts and to maintain their competitive positions in the international ﬁnancial markets. Lending decisions were frequently made with little judgement regarding the credit quality of the borrowing country. As a result, the debt repayment problems of Poland and other Communist Block countries in the early 1980s, and the debt moratoria announced by the Mexican and Brazilian governments in the fall of 1982,

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caused major and long-lasting effects on the balance sheets and proﬁts of the commercial banks in some countries (Saunders and Lange, 1996). In light of these events, the concept of country risk, or the likelihood that a sovereign state or borrower from a particular country may be unable and/or unwilling to fulﬁl their obligations towards one or more foreign lenders and/or investors (Krayenbuehl, 1985), has become a topic of major concern for the international ﬁnancial community. A lending decision to a party residing in a foreign country is a two-step decision. Apart from assessing the underlying credit quality of the borrower, as would be done for a domestic loan, the lender must assess the risk associated with the country in which the borrower resides. Should the credit risk or quality of the borrower be assessed as good but the country risk assessed as bad, the loan should not be made. Thus, Saunders and Lange (1996) argue that considerations of country risk dominate those of private credit risk in international lending decisions. As discussed in Hoti and McAleer (2004), country risk may be prompted by a number of country-speciﬁc factors or events. There are three major components of country risk, namely economic, ﬁnancial and political risk. The country risk literature argues that economic, ﬁnancial and political risks are interdependent. Overholt (1982) states that international business scenarios are generally political– economic as businesses and individuals are interested in the economic consequences of political decisions. The risk involved with lending to a sovereign government is known as sovereign risk. According to Ghose (1988), sovereign risk emerges when a sovereign government repudiates its overseas obligations, and when a sovereign government prevents corporations and individuals residing in the country from fulﬁlling such obligations. In particular, sovereign risk arises when the repudiation occurs in cases where the country is in a ﬁnancial position to meet its obligations. However, sovereign risk also emerges where countries are experiencing genuine difﬁculties in meeting their obligations. In an attempt to extract concessions from their lenders and to improve rescheduling terms, negotiators sometimes threaten to repudiate their ‘borrowings’ (Bourke, 1990). Political risk is generally viewed as a non-business risk introduced strictly by political forces. Banks and other multinational corporations have identiﬁed political risk as a factor that could seriously affect the proﬁtability of their international ventures (Shanmugam, 1990). Ghose (1988) argues that political risk is analogous to sovereign risk and lies within the broader framework of country risk. Political risk emerges from events such as wars, internal and external conﬂicts, territorial disputes, revolutions leading to changes of government and terrorist attacks around

Introduction

3

the world. Social factors include civil unrests due to ideological differences, unequal income distribution and religious clashes. Shanmugam (1990) gives external reasons as a further aspect of political risk. For instance, if the borrowing nation is situated alongside a country that is at war, the country risk level of the prospective borrower will be higher than if its neighbour were at peace. Although the borrowing nation may not be directly involved in the conﬂict, the chances of spillover effects may exist. Additionally, the inﬂow of refugees would affect the economic conditions in the borrowing nation. In practical terms, Juttner (1995) states that political risk relates to the possibility that the sovereign government may impose foreign exchange and capital controls, additional taxes, and asset freezes or expropriations. Delays in the transfer of funds can have serious consequences for investment returns, import payments and export receipts. Hoti and McAleer (2004) suggest that economic and ﬁnancial risks are also major components of country risk. They include factors such as sudden deterioration in the country’s terms of trade, rapid increases in production costs and/or energy prices, unproductively invested foreign funds, and unwise lending by foreign banks (Nagy, 1988). Changes in the economic and ﬁnancial management of the country are also important factors. These risk factors interfere with the free ﬂow of capital or arbitrarily alter the expected risk-return features for investment. Foreign direct investors are also concerned about disruptions to production, damage to installations and threats to personnel (Juttner, 1995). 1.2. Country risk literature For purposes of evaluating the signiﬁcance of empirical models of country risk, it is necessary to analyse such models according to established statistical and econometric criteria. The country risk literature has been reviewed by Saini and Bates (1984), Eaton and Taylor (1986), Rockerbie (1993) and Hoti and McAleer (2004). Saini and Bates (1984) provide a survey of the quantitative approaches to risk analysis by reviewing the problems in the statistical approaches of published empirical papers. Eaton and Taylor (1986) review the theoretical aspects of numerous papers relating to LDC debt and ﬁnancial crises, with an emphasis on the policy implications to be drawn. While the primary purpose of Rockerbie (1993) is to explain the interest spread on sovereign Eurodollar loans on the basis of various indicators of default risk in lesser developed countries and developed countries, he provides a useful summary of risk indicators in the empirical papers examined. Hoti and McAleer (2004) expand on these surveys by using a number of more recently published contributions

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to the literature on country risk. In particular, they evaluate the empirical contributions to the country risk literature according to econometric and statistical criteria. Chapter 2 is a continuation of these surveys by providing a detailed analysis of the empirical foundations of the published contributions to the literature on country risk. Hoti and McAleer (2004) reviewed 50 published empirical studies on country risk, and gave a classiﬁcation of the 50 empirical studies according to the data and sample sizes used, the pooled and cross-section nature of the data by both the number of countries and the number of time series observations used, model speciﬁcations examined, the choice of dependent and explanatory variables considered, the number of explanatory variables used, econometric issues concerning the recognition, type and number of omitted explanatory variables, the number and type of proxy variables used when variables are omitted, the method of estimation and the use of diagnostic tests of the auxiliary assumptions of the models. Appendices 2.1 and 2.2 to this chapter provides a classiﬁcation of the 50 empirical studies according to descriptions and analyses of the various models, namely the speciﬁc topic considered, the source of the information and the data, the choice of dependent and explanatory variables, recognition of omitted explanatory variables in the empirical studies, the choice of model and method of estimation, the provision of descriptive statistics and diagnostic tests and the range of empirical ﬁndings. 1.3. Risk ratings and rating systems The debt crises of the early 1980s, political changes that occurred in the former Communist Block countries in the late 1980s and early 1990s, the East Asian, East European and Latin American ﬁnancial and banking crises that have occurred since 1997, and ﬁnally the events of 11 September 2001 and their aftermath, show clearly that the risks associated with engaging in international operations have increased substantially. Such events have also become more difﬁcult to analyse and predict for decision makers in the economic, ﬁnancial and political sectors. The importance of country risk analysis is underscored by the existence of several prominent risk rating agencies. Rating agencies compile country risk ratings as measures of the ability and willingness of countries to service their ﬁnancial obligations. However, the accuracy of risk rating agencies with regard to any or all country risk measures is open to question. Country risk rating systems have been evaluated recently by Hoti (2005) and Hoti and McAleer (2004). Chapter 3 is an extension of these

Introduction

5

surveys using information on an additional three risk rating agencies. A qualitative comparison is provided for the country risk rating systems of 10 prominent risk-rating agencies. Seven agencies, namely the Economist Intelligence Unit, Euromoney, Institutional Investor, International Country Risk Guide (ICRG), Moody’s, Political Risk Services and Standard and Poor’s, have been selected as their risk ratings have frequently been used in the country risk literature. The remaining three agencies, namely Fitch IBCA, Business Environment Risk Intelligence S.A. and S.J. Rundt and Associates, have been selected given their major roles in international ﬁnancial market operations. A classiﬁcation of the 10 risk rating agencies is given according to the agency deﬁnition of country risk ratings, number of countries covered, frequency of the risk ratings, number and type of ratings compiled, number and type of risk component variables used, weights assigned to risk components and the range associated with the risk ratings. The chapter also evaluates in detail the quantitative rating system of the ICRG, as a representative of agency rating systems. Such an evaluation permits a critical assessment of the importance and relevance of agency rating systems and risk ratings. 1.4. Risk ratings and risk returns for 120 representative countries In order to assess the relevance and accuracy of the risk ratings compiled by various agencies, it is essential that such ratings are analysed critically. Agency risk ratings play a major role in international ﬁnancial market operations. For this reason, the monograph focuses on monthly economic, ﬁnancial, political and composite risk ratings compiled by the ICRG as representatives of agency risk ratings. One-hundred and twenty countries, for which ICRG ratings are available, have been selected to represent eight geographic regions, namely Central and South Asia, East Asia and the Paciﬁc, East Europe, Middle East and North Africa, North and Central America, South America, Sub-Saharan Africa and West Europe. Chapter 4 provides a detailed evaluation of ICRG risk ratings and risk returns, where the latter is deﬁned as the monthly percentage change in the respective risk ratings. For each of the 120 selected countries, the trends and associated volatility of the four country risk ratings and risk returns are analysed according to economic, ﬁnancial and political environments in the country. This chapter provides, for the ﬁrst time, a comparative assessment of the trends and volatility of country risk ratings for the 120 countries, and highlights the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating.

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1.5. Conditional volatility models for risk ratings and risk returns Time series data relating to risk ratings and risk returns contain both conditional mean and conditional variance (or volatility) components, both of which may vary over time. Volatility is used in risk analysis for examining portfolio selection, asset management, valuation of warrants and options, modelling the premium in forward and futures prices, evaluation of risk spillovers across markets, designing optimal hedging strategies for options and futures markets, and measuring the announcement effects in event studies, among others. Moreover, derivative assets are used to hedge against commodity price risk and to hedge against issued bonds. As such, optimal hedging strategies and an evaluation of the risks underlying risk ratings require knowledge of the volatility of the underlying stochastic process. As volatility is generally unknown, it must be estimated. Such estimated and predicted volatilities are fundamental to risk management in ﬁnancial portfolio models that describe the trade-off between risk and returns. Estimating and testing the volatility associated with risk ratings would seem to be a ﬁrst step in establishing a market for pricing risk ratings as a primary or derivative asset. Conditional volatility has been used to evaluate risk, asymmetric shocks, and leverage effects in economics and ﬁnance. Volatility that is present in country risk ratings will naturally reﬂect risk considerations inherent in such ratings. For this reason, the rate of change in risk ratings, that is, their underlying returns, merits the same attention as has been bestowed on ﬁnancial returns. If these vary over time, they can be modelled using time series methods. Chapter 5 reviews the most recent theoretical results on univariate timevarying models of conditional volatility, and discusses a speciﬁc multivariate model that is due to Hoti et al. (2002). The structure of this model, which includes two well-known multivariate models as special cases, is examined to enable a sensible empirical evaluation of country risk ratings, risk returns and their associated conditional volatility. 1.6. Empirical results Monthly data can capture the time-varying volatility that is inherent in the underlying series. As risk ratings can be treated as indexes, their rates of change, or risk returns, are analysed in the same manner as ﬁnancial returns. Chapter 6 uses monthly ICRG country risk returns to estimate and test univariate and multivariate models of volatility. The univariate and multivariate empirical results enable a validation of

Introduction

7

the regularity conditions underlying the model, highlight the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating, and evaluate the practical usefulness of the ICRG risk ratings. Symmetric and asymmetric univariate time-varying conditional volatility models are estimated for four risk returns, namely economic, ﬁnancial, political and composite, for each of the 120 representative countries. Variations in the degree of volatility for a risk return across countries or a country across risk returns need to be appreciated in order to make optimal investment decisions. The main purpose of this chapter is to estimate the static (or constant) conditional correlations of shocks to risk returns across countries. An understanding of the relationship between country risk ratings and risk returns, and the conditional correlations of shocks between pairs of risk returns, are essential for optimal decision making about foreign investments. Speciﬁcally, a conditional correlation of þ1 means the shocks to the risk returns for two countries move in the same direction. In this case, foreign investors should decide to invest on the basis of the risk ratings of both countries and the country with the higher risk returns. Thus, if both countries have the same risk rating, the foreign investor should choose the country with the higher risk returns. A conditional correlation of 0 means the shocks to the risk returns for two countries are uncorrelated. Foreign investors should decide to invest on the basis of the risk ratings and risk returns of both countries. Thus, if both countries have the same risk returns, the foreign investor should choose the country with the higher risk rating; if both countries have the same risk rating, the foreign investor should choose the country with the higher risk returns. For a conditional correlation of 21, as the shocks to the risk returns for the two countries move in opposite directions, foreign investors should hedge and invest in both countries according to their respective risk ratings. Such issues based on models of conditional volatility have not previously been considered in the country risk literature. 1.7. Conclusion Chapter 7 summarizes the qualitative assessment of country risk literature, agency risk rating systems and country risk ratings and risk returns for 120 selected countries, and the theoretical and empirical results relating to various univariate and multivariate risk returns and corresponding constant conditional correlation coefﬁcients. Suggestions for future research are also presented.

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References Bourke, P. (1990), “Risks in international banking”, in: P. Bourke and B. Shanmugam, editors, An Introduction to Bank Lending, Sydney: Addison-Wesley Business Series. Eaton, J. and L. Taylor (1986), “Developing country ﬁnance and debt”, Journal of Development Economics, Vol. 22, pp. 209– 265. Ghose, T.K. (1988), “How to analyse country risk”, Asian Finance, pp. 61 – 63, October. Hoti, S., F. Chan and M. McAleer (2002), “Structure and asymptotic theory for multivariate asymmetric volatility: empirical evidence for country risk ratings”, Invited Paper Presented at the Australasian Meeting of the Econometric Society, Brisbane, Australia, July 2002. Hoti, S. and M. McAleer (2004), “An empirical assessment of country risk ratings and associated models”, Journal of Economic Surveys, Vol. 18(4), pp. 539– 588. Hoti, S. (2005), “Comparative analysis of risk ratings for the East European region”, Mathematics and Computers in Simulation, in press. Juttner, D.J. (1995), Risk premia in foreign exchange and interest rates, International Finance and Global Investments, 3rd edition, Melbourne: Longman, Chapter 16. Krayenbuehl, T.E. (1985), Country Risk: Assessment and Monitoring, Toronto: Lexington Books. Nagy, P.J. (1988), Country Risk: How to Assess, Quantify, and Monitor It, London: Euromoney Publications. Overholt, W.H. (1982), Political Risk, London: Euromoney Publications. Rockerbie, D.W. (1993), “Explaining interest spreads on sovereign Eurodollar loans: LDCs versus DCs, 1978– 84”, Applied Economics, Vol. 25, pp. 609– 616. Saini, K.G. and P.S. Bates (1984), “A survey of the quantitative approaches to country risk analysis”, Journal and Banking and Finance, Vol. 8, pp. 341– 356. Saunders, A. and H. Lange (1996), Financial Institutions Management, Sydney: Irwin. Shanmugam, B. (1990), “Evaluation of political risk”, in: P. Bourke and B. Shanmugam, editors, An Introduction to Bank Lending, Sydney: Addison-Wesley Business Series.

CHAPTER 2

Country Risk Models: An Empirical Critique Abstract This chapter provides a detailed analysis of the empirical foundations of the published contributions to the literature on country risk. Of the 50 published empirical studies in the country risk literature, a classiﬁcation is given according to descriptions and analyses of the various models, namely the speciﬁc topic considered, the source of the information and the data, the choice of dependent and explanatory variables, recognition of omitted explanatory variables in the empirical studies, the choice of model and method of estimation, the provision of descriptive statistics and diagnostic tests and the range of empirical ﬁndings. Keywords: country risk, agency ratings, risk component variables, debt rescheduling, statistical and econometric criteria, estimation, testing, evaluation, empirical ﬁndings JEL classiﬁcations: C21, C22, C51, E44, F34, O16

2.1. Introduction For purposes of evaluating the signiﬁcance of empirical models of country risk, it is necessary to analyse such models according to established statistical and econometric criteria. The primary purpose of each of these empirical papers is to evaluate the practicality and relevance of the economic, ﬁnancial and political theories pertaining to country risk. An examination of the empirical impact and statistical signiﬁcance of the results of the country risk models will be based on an evaluation of the descriptive statistics relating to the models, as well as the econometric procedures used in estimation, testing and forecasting. The country risk literature has been surveyed previously by Saini and Bates (1984), Eaton and Taylor (1986), Rockerbie (1993) and Hoti and

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McAleer (2004). Saini and Bates (1984) provide a survey of the quantitative approaches to risk analysis by reviewing the problems in the statistical approaches of published empirical papers. In particular, they examine the shortcomings with regard to deﬁnitions of dependent variables, the quality and availability of data, model speciﬁcations, appropriateness of statistical methods and the ability to forecast debtservicing difﬁculties adequately. Eaton and Taylor (1986) review the theoretical aspects of numerous papers relating to LDC debt and ﬁnancial crises with an emphasis on the policy implications to be drawn. Although they do not analyse the empirical aspects of the various papers, they examine the three main issues in empirical applications, namely the determinants of rescheduling, how credit terms are ﬁxed and the factors determining the amounts borrowed. While the primary purpose of Rockerbie (1993) is to explain the interest spread on sovereign Eurodollar loans on the basis of various indicators of default risk in lesser developed countries and developed countries, he provides a useful summary of risk indicators in the empirical papers examined. Hoti and McAleer (2004) expand on these surveys by using a number of more recently published contributions to the literature on country risk. In particular, they evaluate the empirical contributions to the country risk literature according to econometric and statistical criteria. Chapter 2 is a continuation of these surveys by providing a detailed analysis of the empirical foundations of the published contributions to the literature on country risk. Hoti and McAleer (2004) reviewed 50 published empirical studies on country risk, and gave a classiﬁcation of the 50 empirical studies according to the data and sample sizes used, the pooled and cross-section nature of the data by both the number of countries and the number of time series observations used, model speciﬁcations examined, the choice of dependent and explanatory variables considered, the number of explanatory variables used, econometric issues concerning the recognition, type and number of omitted explanatory variables, the number and type of proxy variables used when variables are omitted, the method of estimation and the use of diagnostic tests of the auxiliary assumptions of the models. Appendices 2.1 and 2.2 provides a classiﬁcation of the 50 empirical studies according to descriptions and analyses of the various models, namely the speciﬁc topic considered, the source of the information and the data, the choice of dependent and explanatory variables, recognition of omitted explanatory variables in the empirical studies, the choice of model and method of estimation, the provision of descriptive statistics and diagnostic tests and the range of empirical ﬁndings.

Country Risk Models: An Empirical Critique

11

2.2. Classiﬁcation of the data Hoti and McAleer (2004) analyse 50 published empirical papers over the last three decades in referred journals and other sources. Although the literature seems to have started in the early 1970s, there were 16 papers published in the 1980s, a further 30 papers published in the 1990s, and with the two most recent papers having been published in the early 2000s. There does not appear to be a leading journal in the literature on country risk. In Table 2.1 (which follows from Table 1 in Hoti and McAleer (2004)), the 50 studies are classiﬁed according to the type of data used, namely cross-section or pooled, which combines time series and cross-section samples. As can be seen from Appendix 2.1, the common sources of information are the International Monetary Fund, Bank for International Settlements, various sources of the World Bank, Euromoney, Institutional Investor, Moody’s, Standard and Poor’s and various country-speciﬁc statistical bureaux. Almost three-quarters of the studies are based on pooled data, with the remaining one-quarter based on cross-section data. Table 2.2 classiﬁes the 34 studies using pooled data according to the number of countries, which varies from 5 to 95 countries, with a mean of 48 and median of 47, while the frequency of occurrence of each number is close to 1. The same 34 studies using pooled data are classiﬁed according to the number of annual and semi-annual observations in Tables 2.3 and 2.4, respectively. For the annual observations, the 19 data sets range reasonably smoothly from 5 to 24 years. The mean, median and mode of the number of observations are 12, 11 and 5, respectively, while the frequency of occurrence of each number varies between 1 and 5. As reported in Table 2.4, the range of the eight data sets using semiannual observations is 8 – 38 half-years, with the mean and median and mode of the number of observations being 18.5 and 17, respectively. Although there are not many such studies, the typical number of observations lies just below 20.

Table 2.1. Classiﬁcation by type of data used Type of Data

Frequency

Pooled Cross-section

34 16

Total

50

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Table 2.2. Classiﬁcation of pooled data by number of countries Number of Countries 5 16 17 19 24 25 26 27 30 32 33 39 40 41 43 47 48 54 55 56 59 60 65 68 74 75 79 80 85 90 95 Total

Frequency 1 1 1 1 1 1 1 2 1 1 2 1 2 1 1 2 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 37

Note: Three studies used two data sets.

Tables 2.5 and 2.6 classify the studies using cross-section data according to the number of countries and the number of time series observations, respectively. In Table 2.5, the number of countries used increases systematically from 18 to 93, with a substantial jump to 143 countries in one study. Furthermore, one study did not report the number of countries used, while another study used data on 892 municipalities. Of the remaining 16 studies, the range is 18– 143 countries, with mean 55.3 and median 50.5.

Country Risk Models: An Empirical Critique

13

Table 2.3. Classiﬁcation of pooled data by number of annual observations Number of Observations 5 8 9 10 11 12 13 14 15 16 17 18 19 22 24

Frequency 5 3 2 3 4 3 1 1 1 1 1 1 3 1 1

Total

31

Notes: (1) One study used two annual data sets. (2) Two studies used one annual data set and one semi-annual data set. (3) One study used one annual data set, one semi-annual data set and one monthly data set.

Table 2.4. Classiﬁcation of pooled data by number of semi-annual observations Number of Observations

Frequency

8 16 17 22 38

2 1 2 2 1

Total

8

Notes: (1) One study used two semi-annual data sets. (2) Two studies used one annual data set and one semi-annual data set. (3) One study used one annual data set, one semi-annual data set and one monthly data set.

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Table 2.5. Classiﬁcation of cross-section data by number of countries Number of Countries

Frequency

18 20 27 29 30 35 45 49 52 70 71 88 93 143 892 Unstated Total

1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 18

Notes: (1) One study used three data sets. (2) The sample with 892 observations refers to municipalities rather than countries.

Table 2.6. Classiﬁcation of cross-section data by number of time series observations Number of Observations

Frequency

1 2 3 4 5 7 8 10 11 20 23

10 4 1 2 5 1 1 1 1 2 1

Total

29

Note: More than one time series data set was used in some studies.

Country Risk Models: An Empirical Critique

15

There are 29 data sets using time series observations in Table 2.6, with range 1 – 23, mean 5.3, median 3 and mode 1. Indeed, the most commonly used number of time series observations is 1, with a frequency of 10 in the 29 data sets, so that more than one-third of the cross-section data sets used are based on a single year. However, there are two studies which use 20 or more times series observations. 2.3. Theoretical and empirical model speciﬁcations For purposes of analysing the empirical content of the published papers in the country risk literature, it will be convenient to proceed as follows. The general country risk model typically estimated, tested and evaluated is given as f ðYt ; Xt ; ut ; bÞ ¼ 0

ð2:1Þ

in which f ð·Þ is an unspeciﬁed functional form, Y is the designated (vector of) endogenous variables, X is the (vector of) exogenous variables, u is the (vector of) errors, b is the vector of unknown parameters and t ¼ 1; …; n is the number of observations. As will be discussed below, Equation (2.1) is typically given as a linear or log – linear regression model, or as a logit, probit or discriminant model. The elements of Y and X will also be discussed below. Deﬁning the information set at the end of period t 2 1 as It21 ¼ ½Yt21 ; Yt22 ; Yt23 ; …; Xt ; Xt21 ; Xt22 ; Xt23 ; … ; the assumptions of the classical regression model are typically given as follows: (A1) Eðut Þ ¼ 0 for all t (i.e. the model is correctly speciﬁed); (A2) constant unconditional and conditional variances of ut (i.e. no heteroscedasticity or time-varying conditional variances); (A3) serial independence (i.e. no covariation between ut and us for t – s); (A4) X is weakly exogenous (i.e. there is no covariation between Xt and us for all t and s); (A5) normally distributed shocks; (A6) constant parameters; (A7) Y and X are both stationary processes, or are cointegrated if both are non-stationary processes. Diagnostic tests play an important role in modern empirical econometrics, and are used to check the adequacy of a model through testing the underlying assumptions (see, for example, McAleer (1994)). The standard diagnostic checks which are used to test assumptions (A1)– (A7) are various tests of functional form misspeciﬁcation, heteroscedasticity, conditional

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volatility, serial correlation, weak exogeneity, symmetry (or deviation from the third moment), kurtosis (or deviations from the fourth moment), higher order moments of the distribution for non-normality, constancy of parameters, structural change, time-varying variances, covariances and correlations, unit root tests and tests of cointegration. There is, in general, little or no theoretical basis in the literature for selecting a particular model of country risk. In empirical analysis, however, computational convenience and the ease of interpretation of models are primary considerations for purposes of model selection. Of the 70 models used in the 50 studies, which are reported in Table 2.7 (which follows from Table 7 in Hoti and McAleer (2004)), all but six are univariate models. The most popular model in the literature is the logit model, which is used 23 times, followed by the probit, discriminant and Tobit models, which are used 10, 7 and 3 times, respectively. Thus, more than half of the models used in the literature are probability-based models. It is instructive to note that most papers that report estimates of these probability-based models provide little further information regarding the performance of the models. Given the popularity of the linear and log – linear regression models in empirical economic research, it is surprising to Table 2.7. Classiﬁcation by type of model Model

Frequency

Only linear single equations Only log – linear single equations Both linear and log –linear single equations Logit Probit Discriminant model Tobit System of equations Artiﬁcial neural network model Others

4 2 2 23 10 7 3 6 2 11

Total

70

Notes: (1) More than one model was used in some studies and two studies used no model. (2) The ‘Others’ category includes one entry for each of multigroup hierarchical discrimination model, two-way error components model, random-effect error component equations, naive model, combination model, G-Logit model, nested trinomial logit, sequential-response logit, unordered-response logit, classiﬁcation and regression trees and cluster analysis.

Country Risk Models: An Empirical Critique

17

see that the linear regression model is used four times, the log – linear regression model is used only twice, and both regression models are used in the same study only twice. The artiﬁcial neural network model is also used twice. Of the remainder, the multi-group hierarchical discrimination model, two-way error components model, random-effect error component equations, naive model, combination model, G-logit model, nested trinomial logit, sequential-response logit, unordered-response logit, classiﬁcation and regression trees and cluster analysis are used once each. The dependent variable for purposes of analysing country risk is broadly classiﬁed as the ability to repay debt. Of the different types of dependent variables given in Table 2.8 (which follows from Table 8 in Hoti and McAleer (2004)), with more than one dependent variable being used in some studies, the most frequently used variable is debt rescheduling, which is used 36 times. According to Appendix 2.1, this dependent variable is deﬁned as the probability of debt rescheduling (as a proxy for debt default), the probability of partial reneging when a borrower has decided to reschedule, trichotomous variable of debt rescheduling, the probability of general, commercial, ofﬁcial and band debt rescheduling (in the current year or in the future), the probability of debt default, and discriminant score of whether a country belongs to a rescheduling or nonrescheduling group. The second most frequently used variable is agency risk ratings, which is used 18 times. According to Appendix 2.1, this dependent variable in empirical analyses is deﬁned as Institutional Investor, Euromoney, Standard and Poor’s, Moody’s and Economist Intelligence Unit country or municipality risk ratings, and the average of agency country risk ratings. Ten types of dependent variable are used more than once, with debt arrears (deﬁned as the limit on debt arrears), dummy for signiﬁcant debt arrears, probability of experiencing signiﬁcant debt arrears and probability of emerging debt-servicing arrears being used four times each, and average value of debt rescheduling, exchange rate movements, fundamental valuation ratios, demand for debt and supply of debt being used three times each. Dependent variables, such as the propensity to obtain agency municipality credit risk ratings, public debt to private creditors, total reserves and total or relative bond spread, are used twice each. The remaining 10 types of dependent variable, which are used once each, include weighted average loan spread, spread over LIBOR, yield spreads of international bonds, payment interruption likelihood index, sovereign loan default, credit risk rating, income classiﬁcation, stock returns, secondary market price of foreign debt and dummy for debt crisis. There are three types of explanatory variables used in the various empirical studies, namely economic, ﬁnancial and political. Treating country risk

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Table 2.8. Classiﬁcation by type of dependent variable useda Type

Frequency

Debt reschedulingb Agency country risk ratingsc Debt arrearsd (Average) value of debt rescheduling Exchange rate movements Fundamental valuation ratios Demand for debt Supply of debt Propensity to obtain agency municipality credit risk ratings Public debt to private creditors Total reserves (Relative) bond spreads Weighted average loan spread Spread over LIBOR Yield spreads of international bonds Payment interruption likelihood index Sovereign loan default Credit risk rating Income classiﬁcation Stock returns Secondary market price of foreign debt Dummy for debt crisis

36 18 4 3 3 3 3 3 2 2 2 2 1 1 1 1 1 1 1 1 1 1

Total

91

a

More than one dependent variable was used in some studies. Includes variables deﬁned as the probability of debt rescheduling (as proxy for debt default), the probability of partial reneging when a borrower has decided to reschedule, trichotomous variable of debt rescheduling, the probability of general, commercial, ofﬁcial and band debt rescheduling (in the current year or in the future), the probability of debt default, and discriminant score of whether a country belongs to a rescheduling or nonrescheduling group. c Refers to Institutional Investor, Euromoney, Standard and Poor’s, Moody’s and Economist Intelligence Unit country or municipality credit risk ratings and average agency country risk ratings. d Includes one entry for each of limit on debt arrears, dummy for signiﬁcant debt arrears, probability of experiencing signiﬁcant debt arrears and probability of emerging debtservicing arrears. b

variables as economic and/or ﬁnancial, and regional differences as political, Tables 2.9 and 2.10 present the numbers of each type of variable and their frequency (for further details, see Appendix 2.1). In Table 2.9, the number of economic and ﬁnancial variables ranges steadily from 2 to 23, followed by a jump to 32, with a mean of 11.5, median of 8 and mode of 6. Seven of the 19

Country Risk Models: An Empirical Critique

19

Table 2.9. Classiﬁcation by number of economic and ﬁnancial explanatory variables Number 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 18 20 23 32

Frequency 3 3 4 2 7 3 5 2 2 1 6 3 1 1 3 1 1 1 1

Total

50

Note: Country risk indicators are treated as economic and/or ﬁnancial variables.

Table 2.10. Classiﬁcation by number of political explanatory variables Number

Frequency

0 1 2 3 4 5 6 8 10 11 13

30 4 4 1 2 2 3 1 1 1 1

Total

50

Note: Regional differences are treated as political variables.

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S. Hoti and M. McAleer

sets of economic and ﬁnancial variables have a frequency of 1, with a frequency of 2 occurring three times, a frequency of 3 occurring ﬁve times and frequencies of 4, 5 and 6 occurring once each. In Table 2.10, the number of political variables ranges steadily from 0 to 13, with a mean of 1.86, median of 0 and mode of 0. The absence of any political variable occurs 30 times in the 50 studies, which is quite striking given the importance of political factors on a country’s risk rating. Of the remaining 10 sets of political variables, two have a frequency of 4, one has a frequency of 3, two have a frequency of 2 and ﬁve have a frequency of 1. As reported in Appendix 2.1, hundreds of different economic, ﬁnancial and political explanatory variables have been used in the 50 separate studies. The set of economic and ﬁnancial variables includes indicators for country risk ratings, debt service, domestic and international economic performance, domestic and international ﬁnancial performance, monetary reserves and structural differences. Indicators for country political risk ratings, domestic and international armed conﬂict, political events and regional differences are used in the set of political variables. Unavailability of the required data means that proxy variables have frequently been used in place of the unobserved variables. Tables 2.11 and 2.12 (which follows from Table 12 in Hoti and McAleer (2004)) are concerned with the important issue of omitted explanatory variables in the 50 studies. It is well known that, in general, omission of relevant explanatory variables from a linear regression model yields biased estimates of the coefﬁcients of the included variables, unless the omitted

Table 2.11. Classiﬁcation by recognition of omitted explanatory variables Number Omitted

Frequency

0 1 2 3 4 8

30 13 2 2 2 1

Total

50

Note: The classiﬁcation is based on explicit recognition of omitted explanatory variables, and is used primarily as a check of consistency against the number of proxy variables used in the corresponding studies.

Country Risk Models: An Empirical Critique

21

Table 2.12. Classiﬁcation by type of omitted explanatory variables Omitted Variable

Frequency

Economic and ﬁnancial factors Political factors

28 11

Total

39

Note: The various omitted variables are classiﬁed according to whether they are predominantly economic and ﬁnancial or political in nature.

variables are uncorrelated with each of the included explanatory variables. For non-linear models, consistency replaces unbiasedness as a desirable statistical characteristic of an estimation method. In some studies, there is an indication of the various types of variables that are recognized as being important. Nevertheless, some of these variables have been omitted because they are simply unavailable. The classiﬁcation in Table 2.11 is by recognition of omitted explanatory variables, where the recognition is determined according to whether there is an explicit statement in the study (for further details see Appendix 2.2). Such an explicit recognition of omitted explanatory variables is used primarily as a check of consistency against the number of proxy variables used. Of the 50 studies in Table 2.11, exactly three-ﬁfths did not explicitly recognize that any variables had knowingly been omitted, with the remaining 20 studies recognizing that 39 explanatory variables had been omitted. The number of explanatory variables explicitly recognized as having been omitted varies from 1 to 8. Including and excluding the 30 zero entries for omitted explanatory variables give mean numbers omitted of 0.78 and 1.95, respectively, medians of 0 and 1 and modes of 0 and 1. Thirteen of the 20 studies, which explicitly recognized the omission of explanatory variables, noted that a single variable had been omitted. Overall, this casts considerable doubt on the statistical adequacy of the empirical ﬁndings in the country risk literature. As can be seen in Appendix 2.2, the classiﬁcation in Table 2.12 is given according to the type of omitted explanatory variable, which is interpreted as predominantly economic and ﬁnancial or political. More than two-thirds of the omitted explanatory variables are predominantly economic and ﬁnancial in nature, and the remaining one-third is predominantly political. Somewhat surprisingly, very few studies stated dynamics as having been

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Table 2.13. Classiﬁcation by number of proxy variables used Number 0 1 2 3 4 5 6 7 Total

Frequency 2 7 4 2 1 1 2 1 20

Note: Two studies explicitly recognized the omission of explanatory variables but used no proxy variables.

omitted from the analysis, even though most did not explicitly incorporate dynamics into the empirical speciﬁcations. Ignoring dynamic factors in the model speciﬁcation will mean that differences between short- and long-run effects of shocks in the system will not be measured adequately. As important economic, ﬁnancial and political explanatory variables have been recognized as having been omitted from two-ﬁfths of the 50 studies (see Table 2.11), proxy variables have been used in most of these studies. Tables 2.13 and 2.14 (which follows from Table 14 in Hoti and McAleer (2004)) are concerned with the issues of the number and type of proxy variables used. The problems associated with the use of ordinary least squares (OLS) to estimate the parameters of linear models in the presence of one or more proxy variables are generally well known in the econometrics literature, but extensions to non-linear models, which dominate the literature on country risk, are not yet available. Nevertheless, Table 2.14. Classiﬁcation by type of proxy variables used Proxy Variables

Frequency

Economic and ﬁnancial factors Political factors

34 15

Total

49

Note: Some studies used economic, ﬁnancial and political proxy variables.

Country Risk Models: An Empirical Critique

23

as a guide for analysis, the basic results are outlined below. These results are of special concern as two-ﬁfths of the studies explicitly recognize the omission of at least one explanatory variable, and hence are suggestive of misleading estimates and biased inferences. In the case where only one proxy variable is used to replace a variable which is unavailable, the well-known econometric results are as follows: (1) the absolute bias in the estimated coefﬁcient of the proxy variable is less than the case where the proxy variable is excluded; (2) the absolute bias in the estimated coefﬁcient of the correctly measured variable is less than in the case where the proxy variable is excluded; (3) a reduction in measurement error is beneﬁcial; and (4) it is preferable to include the proxy variable than to exclude it. When two or more proxy variables are used to replace two or more variables that are unavailable, it is also well known that the four basic results stated above will not necessarily continue to hold. Thus, among other outcomes, the absolute bias in the estimated coefﬁcients of both the correctly measured and incorrectly measured variables may be higher if two or more proxy variables are not used than when they are used, a reduction in measurement error may not be beneﬁcial, and it may not be preferable to include two or more proxy variables than to exclude them. The reason for the different outcomes is that the covariation in two or more measurement errors may exacerbate the problem of measurement error rather than containing it. For practical purposes, therefore, omission of more than one explanatory variable can lead to serious problems of biased estimates and invalid inferences. Given the description of the unobservable and/or unavailable explanatory variables in Appendix 2.1, Table 2.13 classiﬁes the 20 studies by the use of proxy variables, which ranges uniformly from 1 to 7. Including and excluding the 2 zero entries for the number of proxy variables used give mean numbers omitted of 2.45 and 2.72, respectively, a median of 2 in each case, and a mode of 1 in each case. As 11 of the 20 studies explicitly recognized the omission of explanatory variables by using two or more proxy variables, a high proportion of the published studies would appear to suffer from statistically inadequate and misleading estimates and inferences. By comparison with Table 2.11, in which 13 of the 20 studies explicitly recognized the omission of a single explanatory variable, Table 2.13 shows that only seven studies used a single proxy variable. Otherwise, the results in Tables 2.11 and 2.13 are reasonably similar. The classiﬁcation in Table 2.14 is given according to the type of proxy variable used, which is interpreted as comprising predominantly economic and ﬁnancial or political factors. More than two-thirds of the proxy variables are predominantly economic

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Table 2.15. Classiﬁcation by method of estimation Method

Frequency

OLS ML Heckman’s two-step procedure Discriminant methods Others

14 35 2 3 17

Total

71

Notes: (1) More than one estimation method was used in some studies. (2) The ‘Others’ category includes entries for, among others, propagation algorithm, regression-based technique, approximation, minimax, Bayesian, optimal minimum distance, stepwise optimization, binary splits, jackknife methods and OLS and WLS.

and ﬁnancial in nature, and the remaining one-third is predominantly political, which is consistent with the results given in Table 2.12. The information in Table 2.15 (which follows from Table 15 in Hoti and McAleer (2004)) gives the classiﬁcation by method of estimation. More than one estimation method is used in some studies. According to Appendix 2.2, ﬁve categories are listed, namely OLS, maximum likelihood (ML), Heckman’s two-step procedure, discriminant methods and others, which includes entries for, among others, propagation algorithm, regression-based techniques, approximation, minimax, Bayesian, optimal minimum distance, stepwise, optimization, binary splits, jack-knife methods and OLS and WLS. Even though logit, probit and Tobit models in Table 2.7 are used 40 times in total, ML is used for estimation purposes only 35 times. Moreover, while linear and log– linear models are used only seven times in total in Table 2.7, OLS is used 14 times in Table 2.15 (15 times if both OLS and WLS are included). Finally, while discriminant models are used seven times in Table 2.7, discriminant estimation is used only three times in Table 2.15. Finally, the classiﬁcation in Table 2.16 (which follows from Table 16 in Hoti and McAleer (2004)) is by use of diagnostics to test one or more auxiliary assumptions of the models. The role of diagnostic tests has become well established in the econometrics literature in recent years, and plays an increasingly prominent role in modern applied econometrics

Country Risk Models: An Empirical Critique

25

Table 2.16. Classiﬁcation by use of diagnostics Type of Diagnostics

Frequency

None Others

42 8

Total

50

Note: The ‘Others’ category includes entries for WLS and heteroscedasticity, White’s standard errors for heteroscedasticity, White’s covariance matrix for heteroscedasticity, Chow test, transformation for non-normality, Hajivassiliou test for exogeneity and serial correlation.

(see McAleer (1994) for further details). In short, a failure to provide diagnostic information for an estimated model is widely regarded as a statistical deﬁciency in empirical analysis. Most diagnostic tests of the auxiliary assumptions are quite standard, and are available in widely used econometric software packages. Given the information in Appendix 2.2, it is quite unbelievable that 42 of the 50 studies did not report any diagnostic tests whatsoever. Of the eight which did report any diagnostic tests at all, there were two entries for White’s standard errors for heteroscedasticity, and one entry for each of WLS and heteroscedasticity, transformation for non-normality, White’s covariance matrix for heteroscedasticity, Chow test, Hajivassiliou’s test for exogeneity and serial correlation. This is of serious concern, especially as the maximum likelihood (ML) method is known to lack robustness to wide range of departures from the stated assumptions. Nevertheless, ML has been used 35 times. Models such as the logit and probit are also sensitive to departures from the underlying logistic and normal densities, respectively, so that the underlying assumptions should be checked rigorously. As the use of diagnostics has generally been ignored in the country risk literature, a high proportion of the empirical results should be interpreted with both caution and scepticism. 2.4. Empirical ﬁndings Of the 91 types of dependent variables used in the 50 studies, the information in Appendix 2.1 shows that 27 studies examined debt rescheduling on 36 occasions and 17 considered country risk ratings on 18

26

S. Hoti and M. McAleer

Table 2.17. Types of variables used in debt rescheduling Variables

Frequency

Economic Financial Political

27 27 9

Number of studies

27

occasions (see Table 2.8 for deﬁnitions of these two types of variables). Table 2.17 (which follows from Table 20 in Hoti and McAleer (2004)) examines the 27 studies concerned with debt rescheduling. Three types of variables were used, namely economic, ﬁnancial and political. While the economic and ﬁnancial variables were used in each of the 27 studies, political variables were used in only nine studies. Table 2.18 (which follows from Table 21 in Hoti and McAleer (2004)) presents the number of variables used in debt rescheduling, as well as their frequency. All three variables have been used in nine studies, two of the three variables were used in the remaining 18 studies, and no study used only one of the three variables. Table 2.19 (which follows from Table 17 in Hoti and McAleer (2004)) reports four types of risk component variables used in the 17 country risk ratings studies, namely economic, ﬁnancial, political and composite. Composite risk variables are ratings or aggregates that comprise economic, ﬁnancial and political risk component variables, and were used in all 17 studies. Of these studies, only two did not use economic variables and only one did not use ﬁnancial variables. Political variables have been used less

Table 2.18. Frequency of types of variables used in debt rescheduling Risk Components Used

Frequency

3 2 1

9 18 0

Total

27

Country Risk Models: An Empirical Critique

27

Table 2.19. Risk component variables used in country risk ratings Variables

Frequency

Economic Financial Political Composite

15 16 10 17

Number of studies

17

frequently, namely in 10 studies. Thus, the economic and ﬁnancial variables would seem to be more important than the political variables in explaining country risk rating. Table 2.20 (which follows from Table 18 in Hoti and McAleer (2004)) presents the number of country risk components used, as well as their frequency. All four country risk components have been used in 10 studies, four studies used variables representing three risk components, three studies used variables representing two risk components, and no study used variables representing only one risk component. In Table 2.21 (which follows from Table 19 in Hoti and McAleer (2004)), the 17 studies are classiﬁed according to the risk rating agency they used, namely Institutional Investor, Euromoney, Moody’s Standard and Poor’s, International Country Risk Guide, Economist Intelligence Unit and Political Risk Services. A qualitative comparison of the risk rating systems used by these seven rating agencies, as well as three other rating agencies, is given in Chapter 3.

Table 2.20. Frequency of risk component variables used in country risk ratings Risk Components Used

Frequency

4 3 2 1

10 4 3 0

Total

17

28

S. Hoti and M. McAleer

Table 2.21. Agency data used Agency Institutional Investor Euromoney Moody’s Standard and Poor’s International Country Risk Guide Economist Intelligence Unit Political Risk Services

Frequency 13 6 2 2 2 1 1

Note: Some studies used data from more than one agency.

2.5. Conclusion The signiﬁcance of the 50 published empirical papers in the country risk literature was evaluated according to established statistical and econometric criteria used in estimation, evaluation and forecasting. Such an evaluation permitted a critical assessment of the relevance and practicality of the economic, ﬁnancial and political theories pertaining to country risk. Overall, country risk studies are based on pooled or cross-section type of data. The two most frequently used dependent variables are the probability of debt rescheduling and agency country risk ratings. There are three types of explanatory variables used, namely economic, ﬁnancial and political variables. More than two-thirds of the omitted explanatory variables and proxy variables used are economic and ﬁnancial in nature. In terms of the preferred country risk model, logit followed by probit and discriminant are the most popular models. While logit, probit and Tobit models are used 40 times in total, the ML estimation method is used only 35 times. Moreover, while linear and log – linear models are used only seven times in total, OLS is used 16 times. Hence, there is a disparity between the model speciﬁcation examined and the appropriate method of estimation. Finally, diagnostic testing has generally been ignored in the country risk literature. Overall, in the absence of testing the validity of the underlying assumptions of the surveyed country risk models, the empirical results should generally be interpreted with both caution and scepticism. References Abdullah, F.A. (1985), “Development of an advanced warning indicator of external debt servicing vulnerability”, Journal of International Business Studies, Vol. 16(3), pp. 135– 141.

Country Risk Models: An Empirical Critique

29

Backer, A. (1992), “Country balance sheet data vs. traditional macro variables in a logit model to predict debt rescheduling”, Economics Letters, Vol. 38, pp. 207– 212. Balkan, E.M. (1992), “Political instability, country risk and probability of default”, Applied Economics, Vol. 24(9), pp. 999– 1008. Brewer, T.L. and P. Rivoli (1990), “Politics and perceived country creditworthiness in international banking”, Journal of Money, Credit and Banking, Vol. 22(3), pp. 357– 369. Burton, F.N. and H. Inoue (1985), “An appraisal of the early-warning indicators of sovereign loan default in country risk evaluation systems”, Management International Review, Vol. 25(1), pp. 45 – 56. Cantor, R. and F. Packer (1996), “Determinants and impact of sovereign credit ratings”, FRBNY Economic Policy Review, October, pp. 37 – 53. Chattopadhyay, S.P. (1997), “Neural network approach for assessing country risk for foreign investment”, International Journal of Management, Vol. 14(2), pp. 159– 167. Citron, J.T. and G. Nickelsburg (1987), “Country risk and political instability”, Journal of Development Economics, Vol. 25, pp. 385– 392. Cooper, J.C.B. (1999), “Artiﬁcial neural networks versus multivariate statistics: an application from economics”, Journal of Applied Statistics, Vol. 26(8), pp. 909–921. Cosset, J.C. and J. Roy (1991), “The determinants of country risk ratings”, Journal of International Business Studies, Vol. 22(1), pp. 135– 142. de Bondt, G.J. and C.C.A. Winder (1996), “Countries’ creditworthiness: an indicator from a probit analysis”, De Economist, Vol. 144(4), pp. 617– 633. de Haan, J., C.L.J. Siermann and E. van Lubek (1997), “Political instability and country risk: new evidence”, Applied Economics Letters, pp. 703– 707. Doumpos, M. and C. Zopounidis (2001), “Assessing ﬁnancial risks using a multicriteria sorting procedure: the case of country risk assessment”, Omega, Vol. 29(1), pp. 97– 109. Easton, S.T. and D.W. Rockerbie (1999), “What’s in a default? Lending to LDCs in the face of default risk”, Journal of Development Economics, Vol. 58, pp. 319– 332. Eaton, J. and M. Gersovitz (1980), “LDC participation in international ﬁnancial markets: debt and reserves”, Journal of Development of Economics, Vol. 7, pp. 3 – 21. Eaton, J. and M. Gersovitz (1981), “Debt with potential repudiation: theoretical and empirical analysis”, Review of Economic Studies, Vol. 48, pp. 289– 309. Eaton, J. and L. Taylor (1986), “Developing country ﬁnance and debt”, Journal of Development Economics, Vol. 22, pp. 209– 265. Edwards, S. (1984), “LDC foreign borrowing and default risk: an empirical investigation, 1976– 80”, The American Economic Review, Vol. 74(4), pp. 726– 734. Erb, C.B., C.R. Harvey and T.E. Viskanta (1996), “Political risk, economic risk and ﬁnancial risk”, Financial Analysts Journal, November/December, pp. 29 – 46. Feder, G. and R.E. Just (1977), “A study of debt servicing capacity applying logit analysis”, Journal of Development Economics, Vol. 4, pp. 25– 38. Feder, G. and L.V. Uy (1985), “The determinants of international creditworthiness and their policy responses”, Journal of Policy Modelling, Vol. 7(1), pp. 133– 156. Feder, G., R.E. Just and K. Ross (1981), “Projecting debt servicing capacity of developing countries”, Journal of Financial and Quantitative Analysis, Vol. 16(5), pp. 651– 669. Frank, C.R. and W.R. Cline (1971), “Measurement of debt servicing capacity: an application of discriminant analysis”, Journal of International Economics, Vol. 1, pp. 327– 344. Hajivassiliou, V.A. (1987), “The external debt repayments problems of LDC’s: an econometric model based on panel data”, Journal of Econometrics, Vol. 36, pp. 205– 230.

30

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Haque, N.U., M.S. Kumar, N. Mark and D.J. Mathieson (1996), “The economic content of indicators of developing country creditworthiness”, International Monetary Fund Staff Papers, Vol. 43(4), pp. 688– 724. Hernandez-Trillo, F. (1995), “A model-based estimation of the probability of default in sovereign credit markets”, Journal of Development Economics, Vol. 46, pp. 163– 179. Hoti, S. and M. McAleer (2004), “An empirical assessment of country risk ratings and associated models”, Journal of Economic Surveys, Vol. 18(4), pp. 539– 588. Kharas, H. (1984), “The long-run creditworthiness of developing countries: theory and practice”, The Quarterly Journal of Economics, August, pp. 415–439. Kugler, P. (1984), “Economic indicators and debt reschedulings 1977– 1981: empirical results from a logit model”, Rivista Internazionale di Scienze Economiche e Commerciali, Vol. 10(11), pp. 992– 1005. Kutty, G. (1990), “Logistic regression and probability of default of developing countries debt”, Applied Economics, Vol. 21, pp. 1649– 1660. Lanoie, P. and S. Lemarbre (1996), “Three approaches to predict the timing and quantity of LDC debt rescheduling”, Applied Economics, Vol. 28, pp. 241– 246. Lee, B.C. and J.G. Powell (1995), “A behavioural approach to sovereign debt reschedulings”, Applied Economics Letters, Vol. 2, pp. 64 – 66. Lee, S.H. (1991a), “Ability and willingness to service debt as explanation for commercial and ofﬁcial rescheduling cases”, Journal of Banking and Finance, Vol. 15, pp. 5 – 27. Lee, S.H. (1991b), “Using terms of rescheduling as proxy for partial reneging of LDC’s debt in a test of willingness-to-pay model”, Journal of International Money and Finance, Vol. 10, pp. 457– 477. Lee, S.H. (1993a), “Relative importance of political instability and economic variables on perceived country creditworthiness”, Journal of International Business Studies, Fourth Quarter, pp. 801– 812. Lee, S.H. (1993b), “Are the credit ratings assigned by bankers based on the willingness of LDC borrowers to repay?”, Journal of Development Economics, Vol. 40, pp. 349– 359. Lloyd-Ellis, H., G.W. McKenzie and S.H. Thomas (1989), “Using country balance sheet data to predict debt rescheduling”, Economics Letters, Vol. 31, pp. 173– 177. Lloyd-Ellis, H., G.W. McKenzie and S.H. Thomas (1990), “Predicting the quantity of LDC debt rescheduling”, Economics Letters, Vol. 32, pp. 67 – 73. Mascarenhas, B. and O.C. Sand (1989), “Combination of forecasts in the international context: predicting debt rescheduling”, Journal of International Business Studies, Vol. 20, pp. 539– 552. McAleer, M. (1994), “Sherlock Holmes and the search for truth: a diagnostic tale”, Journal of Economic Surveys, Vol. 8, pp. 317– 370, Reprinted in L. Oxley, D.A.R. George, C.J. Roberts and S. Sayer (eds.) (1995), Surveys in Econometrics, Oxford: Blackwell, pp. 91 – 138. Moon, C.G. and J.G. Stotsky (1993), “Testing the differences between the determinants of Moody’s and Standard and Poor’s ratings: an application of smooth simulated maximum likelihood estimation”, Journal of Applied Econometrics, Vol. 8, pp. 51 –69. Morgan, J.B. (1986), “A new look at debt rescheduling indicators and models”, Journal of International Business Studies, Vol. 17, pp. 37 – 54. Odedokun, M.O. (1995), “Analysis of probability of external debt rescheduling in SubSaharan Africa”, Scottish Journal of Political Economy, Vol. 42(1), pp. 82 – 98. Oetzel, J.M., R.A. Bettis and M. Zenner (2001), “Country risk measures: how risky are they?”, Journal of World Business, Vol. 36(2), pp. 128– 145.

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31

Oral, M., O. Kettani, J.C. Cosset and M. Daouas (1992), “An estimation model for country risk”, International Journal of Forecasting, Vol. 8, pp. 583– 593. Rahnama-Moghadam, M. (1995), “Debt rescheduling in less-developed countries: world or regional crisis?”, The Journal of Developing Area, Vol. 30, pp. 11 –22. Rahnama-Moghadam, M., H. Samavati and L.J. Haber (1991), “The determinants of debt rescheduling: the case of Latin America”, Southern Economic Journal, Vol. 58, pp. 510– 517. Ramcharran, H. (1999), “The determinants of secondary market prices for developing country loans: the impact of country risk”, Global Finance Journal, Vol. 10(2), pp. 173–186. Rivoli, P. and L.T. Brewer (1997), “Political instability and country risk”, Global Finance Journal, Vol. 8(2), pp. 309– 321. Rockerbie, D.W. (1993), “Explaining interest spreads on sovereign Eurodollar loans: LDCs versus DCs, 1978– 84”, Applied Economics, Vol. 25, pp. 609– 616, . Saini, K.G. and P.S. Bates (1984), “A survey of the quantitative approaches to country risk analysis”, Journal and Banking and Finance, Vol. 8, pp. 341– 356. Schmidt, R. (1984), “Early warning of debt rescheduling”, Journal of Banking and Finance, Vol. 8, pp. 357– 370. Scholtens, B. (1999), “On the comovement of bond yield spreads and country risk ratings”, The Journal of Fixed Income, Vol. 8(4), pp. 99 – 103. Somerville, R.A. and R.J. Tafﬂer (1995), “Banker judgement versus formal forecasting models: the case of country risk assessment”, Journal of Banking and Finance, Vol. 19, pp. 281– 297. Tafﬂer, R.J. and B. Abassi (1984), “Country risk: a model for predicting debt servicing problems in developing countries”, Journal of the Royal Statistical Society Series A, Vol. 147(4), pp. 541– 568.

32

Appendix 2.1. Description of models Topic

Abdullah (1985)

Development of an advanced warning indicator of external debt servicing vulnerability

Journal/Source Journal of International Business Studies

Data Cross-sectional data for 20 developing countries Data on international reserves for 1982– 1983 Data on exports for 1976– 1982 Data on inﬂation rate for 1982(9) – 1983(9) Data on exports for 1982– 1983 Data on political variables for 1982– 1983

Dependent Variables Payments interruption likelihood index

Explanatory Variables Unadjusted payments interruption likelihood index: Ratio of international reserves held at the end of the current quarter over international reserves held at the end of the corresponding quarter in the preceding year Ratio of the growth rate of exports during a certain period over the growth rate of external debt in the same period Ratio of the annual inﬂation rate in the current quarter over the annual inﬂation rate in the corresponding quarter of the succeeding year Ratio of exports during the current quarter over export in the corresponding quarter in the preceding year Political adjustment factor: Improvement in political situation No perceptible change in the political situation Deterioration in the political situation Seriously adverse political developments in the country

S. Hoti and M. McAleer

Author(s)

Backer (1992)

Economics Country balance Letters sheet data vs. traditional macro variables in a logit model to predict debt rescheduling

Country balance sheet data (same as in LloydEllis et al. (1989)): Ratio of undisbursed credit commitments over total borrowing Total bank borrowing relative to bank deposits Proportion of each country’s debt relative to total bank lending for the sample Weighted average spreads of reschedulings Dummy variable to reﬂect the changed ﬁnancial position of a country two periods following a rescheduling (equals 1 in the two periods following a rescheduling, 0 otherwise) Dummy variable (equals 1 in a rescheduling period, 0 otherwise) Macroeconomic variables: Weighted average government bond yield of the G7 Exports Stock exchange index GDP of the G-7 Dummy variables: Eastern block countries African, Caribbean or Paciﬁc countries, beneﬁciaries of the Lome-treaty Less developed countries (UN deﬁnition of 1971)

Country Risk Models: An Empirical Critique

Pooled (with Probability of debt semi-annual) data rescheduling by a for 68 developing country countries for 1981(1) – 1988(2) Seasonally adjusted data (?)

(continued)

33

34

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

Political instability, country risk and probability of default

Applied Economics

Pooled data for 33 developing countries for 1970– 1984

Probability of debt rescheduling by a country in a given year (proxy for debt default)

Economic variables: Debt service/exports Reserves/imports Amortization rate Debt outstanding/GNP Interest payments/exports Growth rate of GNP/capita Domestic savings rate Growth rate of exports Current account balance/exports Imports/GNP Growth rate of OECD countries Political variables: Level of democracy (proxy for political events) Level of political instability (proxy for political events)

de Bondt and Winder (1996)

Countries’ creditworthiness: an indicator from a probit analysis

De Economist

Pooled data for 32 developing countries for 1983– 1993

Probability that a country experiences signiﬁcant payment arrears

Debt-service obligations to exports ratio Long-term external debt to exports ratio Change in short-term debt to total debt ratio Portfolio investments to debt-service ratio Direct investments to debt service ratio Investment to GDP ratio Gross domestic savings to GDP ratio

S. Hoti and M. McAleer

Balkan (1992)

Brewer and Rivoli (1990)

Politics and Journal of perceived country Money, Credit creditworthiness in and Banking international banking

Cross-sectional data for 30 developing countries Data on creditworthiness for 1987 Data on economic indicators for 1986 Data on political factors for 1967– 1986

Euromoney country risk ratings (logistically transformed) Institutional Investor country risk ratings (logistically transformed)

Political Variables: Recent political conditions: Number of changes in the head of government between 1982– 1986 (proxy for governmental regime stability) Political rights scores for 1986, (proxy for political legitimacy) Armed conﬂict in progress at the end of 1986 (proxy for armed conﬂicts) Long term political conditions: Number of changes in the head of government between 1967– 1982 (proxy for governmental regime stability) Political rights mean score for 1975– 1986 (proxy for political legitimacy)

Country Risk Models: An Empirical Critique

Externally ﬁnanced ﬁscal surplus to GDP ratio Inﬂation Exchange rate regime aiming at a peg to a basket of currencies or at limited ﬂexibility against the US dollar (leading to low exchange rate ﬂexibility against special drawing rights) Cumulative number of years with current account deﬁcit Degree of concentration of exports Real GDP growth of US (proxy for worldwide economic activity)

(continued)

35

36

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables The number of years conﬂict in progress as of the end of 1986 (proxy for armed conﬂicts) Economic variables: Current account balance as percent of GNP Total external debt as percent of GNP

An appraisal of the Management International early-warning Review indicators of sovereign loan default in country risk evaluation systems

Pooled data Sovereign for 85 developing loan default countries for 1968– 1977

Domestic economy variables: GNP GNP per capita Growth rate of GNP Growth rate of GNP per capita Inﬂation rate Growth rate of money supply Budgetary balances External economy variables: Export volume Export growth Imports Imports growth Export diversiﬁcation Import compressibility Change in foreign exchange reserves Foreign exchange import coverage FDI ﬂows External debt variables: Ratio of outstanding debt to GNP

S. Hoti and M. McAleer

Burton and Inoue (1985)

Ratio of outstanding debt to export Maturity composition of debt Ratio of debt service payments to export Other variables: Political factors Administrative capability Determinants and impact of sovereign credit ratings

FRBNY Economic Policy Review

Cross-sectional data for 49 developed and developing countries Data on economic variables for 1991– 1994 Data on country risk ratings for 1995 Cross-sectional data for 35 countries for 1995

Moody’s country risk ratings S&P country risk ratings Average country risk ratings (average of the two agencies’ ratings) Bond spreads (sovereign bond spreads over US Treasuries)

Per capital income (proxy for factors such as the level of political stability) GDP growth Inﬂation Fiscal balance External debt Indicator for economic development (proxy for the minimum income or development level) Indicator for default history (proxy for credit reputation) All of the above Average ratings Positive announcements Rating changes Moody’s announcements Speculative grade Changes in relative spreads (ﬁrst proxy for anticipation)

Country Risk Models: An Empirical Critique

Cantor and Packer (1996)

(continued)

37

38

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Neural network approach for assessing country risk for foreign investment

Citron and Nickelsburg (1987)

Country risk and Journal of political instability Development Economics

International Journal of Management

Dependent Variables

Explanatory Variables

Data for 78 rating announcements made in 1987– 1994 in 18 countries

Relative bond spreads (yield spreads divided by US Treasury rate)

Cross-sectional data for International Country Risk Guide (ICRG) listed countries for 1988

Country risk rating ICRG economic risk index (categorized based ICRG ﬁnancial risk index on the change in the ICRG political risk index position of the US direct investment in the country in question in the year following the evaluation reported by ICRG)

Pooled data for ﬁve developed and developing countries for 1960– 1983

Probability of default (a condition under which a country will renegotiate the terms or conditions of debt due to an inability to meet the existing debt schedule)

Rating gaps between the agencies (second proxy for anticipation) Other rating announcements (third proxy for anticipation)

Change in GDP Change in balance of payments International reserves Number of changes in government over a 5-years period (proxy for political instability)

S. Hoti and M. McAleer

Chattopadhyay (1997)

Data

Discriminant score of debt rescheduling Probability of debt rescheduling by a country

Average annual growth in real GNP per capital over 1960– 1982 Ratio of short term debt over exports Debt service ratio Ratio of international reserves over total imports

Artiﬁcial neural networks vs. multivariate statistics: an application from economics

Journal of Applied Statistics

Cross section data for 70 developed and developing countries Data on debt rescheduling for 1983 Data on economic variables for 1960– 1982

Cosset and Roy (1991)

The determinants of country risk ratings

Journal of International Business Studies

Euromoney country GNP per capita Cross-sectional data for 71 devel- risk ratings (logisti- Propensity to invest cally transformed) Reserves to imports ratio oped and develCurrent account balance over GNP oping countries Export growth rate Data on Export variability risk ratings Net foreign debt to exports ratio for 1987 Debt service difﬁculties (dummy variable) Data on economic Political instability indicator and political indicators for 1982– 1986

Country Risk Models: An Empirical Critique

Cooper (1999)

(continued)

39

40

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

Assessing ﬁnancial Omega risks using a multicriteria sorting procedure: the case of country risk assessment

Cross-sectional data for 143 developed and developing countries for 1995

Country’s classiﬁcation as high income, upper-middle income, lowermiddle income, or low-income economy (based on comparing the utility of classifying a country into one group as opposed to another)

Current account balance to GDP ratio Export volume growth Gross domestic investment over GDP ratio Import volume growth Inﬂation (GDP deﬂator) Net trade in goods and services Present value of debt to exports of goods and services ratio Present value of debt to GNP ratio Total debt service to GNP ratio Income velocity of money GNP growth Gross international reserves in months of imports

Easton and Rockerbie (1999)

Journal of What’s in a Development default? Lending to LDCs in the face Economics of default

Panel data for 24 developing countries for 1971– 1992

Probability of debt default by a country Probability of debt default scaled by the magnitude of the loss Probability of debt rescheduling by a country

Ratio of accumulated arrears over long-term debt Change in income due to terms of trade effects Ratio of long-term debt to GDP Ratio of foreign reserves to imports Ratio of gross investment to GDP Ratio of trade balance over GDP LIBOR rate Labour participation rate Ratio of net transfers to GDP Estimated optimal loan spread

S. Hoti and M. McAleer

Doumpos and Zopounidis (2001)

LDC participation in international ﬁnancial markets: debt and reserves

Journal of Development Economics

Cross section data for 45 developing countries for 1970 and 1974

Dummy variable for 1982– 1987 Inverse Mill’s ratio (reﬂecting the probability of a loan observation being in the observed loan sample)

Demand for debt Credit ceiling imposed by lenders (debt supply) Per capita real public debt to private creditors Demand for international reserves when the credit constraint is not binding (which is the actual holding of reserves) Demand for international reserves when the credit is constrained (which is the actual holding of reserves)

Percentage variability of exports (measured as the standard error of a regression of natural log of real exports on a constant and time) Ratio of imports to GNP Average growth rate of GNP per capita Natural log of total real GNP per capita Total population Per capita real public debt to ofﬁcial creditors Dummy variable (equals 0 in 1970 and 1 in 1974) Per capita public debt to private creditors

41

(continued)

Country Risk Models: An Empirical Critique

Eaton and Gersovitz (1980)

Binary dependent variable for unobserved loan spread values Weighted average loan spread for a country

42

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

Debt with potential Review of repudiation: theor- Economic etical and empiri- Studies cal analysis

Cross section data for 45 developing countries for 1970 and 1974

Demand for debt Supply of debt (credit ceiling imposed by lenders) Public debt to private creditors

Percentage variability of exports (measured as the standard error of a regression of natural log of real exports on a constant and time) Ratio of imports to GNP Real GNP Total population Growth rate of GNP Real public debt to public institutions Dummy variable (equals 0 in 1970 and 1 in 1974)

Edwards (1984)

LDC foreign borrowing and default risk: an empirical investigation

Pooled data for 19 developing countries for 1976– 1980

Spread over LIBOR (reﬂecting the probability of default by a borrowing country)

Debt to output ratio Ratio of debt service to exports Ratio of international reserves to GNP Loan duration Loan volume Propensity to invest Ratio of the current account to GDP Average propensity to import Growth of per capita GDP GNP per capita Rate of inﬂation Variability of international reserves Rate of devaluation Government expenditure over GNP

American Economic Review

S. Hoti and M. McAleer

Eaton and Gersovitz (1981)

Political risk, economic risk and ﬁnancial risk

Financial Analysts Journal

Pooled (with semi-annual) data for 48 developed and developing countries for 1984(2) – 1995(1) Seasonally adjusted data (?) Pooled (with semi-annual) data for 47 developed and developing countries for 1984(3) – 1995(3) Seasonally adjusted data (?)

Feder and Just (1977)

A study of debt servicing capacity applying logit analysis

Journal of Development Economics

Pooled data for 41 Probability of debt Debt to service ratio developing default by a country Imports to reserves ratio countries for Amortization to debt ratio 1965– 1972 GDP per capita Capital ﬂows to debt service ratio GDP growth Export growth

Semi-annual stock returns Fundamental variables: Book to price ratio Earnings to price ratio Dividend to price ratio Semi-annual stock returns

Risk attributes (lagged): Institutional Investor country risk ratings International Country Risk Guide composite index International Country Risk Guide political index International Country Risk Guide ﬁnancial index International Country Risk Guide economic index Change in the risk attribute: Change in each of the above Risk attributes (lagged) Risk attributes Adjusted risk attributes Book to price ratio

Country Risk Models: An Empirical Critique

Erb et al. (1996)

(continued)

43

44

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

Projecting debt servicing capacity of developing countries

Journal of Financial and Quantitative Analysis

Probability of debt Pooled data for 56 developing rescheduling by a country countries for 1965– 1976

Debt service ratio Foreign exchange reserves to imports ratio Ratio of net non-commercial foreign exchange inﬂows to debt service payments Ratio of commercial foreign exchange inﬂows to debt service payments Exports to GNP ratio Real GNP per capita to US GNP per capita ratio All of the above Square of debt service ratio Square of reserves to imports ratio Square of commercial foreign exchange inﬂows to debt service ratio All of the above Regional country group dummies: Latin-America Middle East/North Africa Asia Advanced Mediterranean Africa (reference group)

Feder and Uy (1985)

The determinants of international creditworthiness and their policy implications

Journal of Policy Modelling

Pooled (with semi-annual) data for 55 developing countries for 1979(2) – 1983(1)

Debt over GDP ratio International reserves over imports ratio Average export growth rate Average GDP growth rate Terms of trade

Institutional Investor country risk ratings (logistically transformed)

S. Hoti and M. McAleer

Feder et al. (1981)

Frank and Cline (1971)

Measurement of Journal of debt servicing International capacity: an appli- Economics cation of discriminant analysis

Pooled data for 26 developing countries for 1960– 1968

de Haan et al. (1997)

Political instability Applied and country risk: Economics new evidence Letters

Pooled data for 65 Probability of debt developing rescheduling by a countries for country 1984– 1993

Discriminant score of a country’s debt servicing capacity (a high score means that a country has rescheduled its debt, and viceversa)

Debt service to exports ratio Growth rate of exports Export ﬂuctuation index Non-compressible imports as a fraction of total imports Per capita income Ratio of debt amortization to total outstanding debt Ratio of imports to GNP Ratio of imports to reserves Reserves to imports ratio Amortization rate Debt/GDP Interest payment over exports Current account over exports Imports over GDP Inﬂation

45

(continued)

Country Risk Models: An Empirical Critique

Export vulnerability index (the degree to which export revenues are concentrated in very few commodities) GNP per capita Dummy for oil exporter countries Dummy for political turmoil Dummy for debt service difﬁculties Time dummies: 1980(1), 1980(2), 1981(1), 1981(2), 1982((1), 1982(2), 1983(1)

46

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

Hajivassiliou (1987)

The external debt repayments problems of LDC’s: an econometric model based on panel data

Journal of Econometrics

Pooled data for 79 developing countries for 1970– 1982

Demand for new loans (equals 1 in presence of IMF support and/or request for a rescheduling)

Lagged explanatory variables: Debt service due to exports ratio Reserves to imports ratio Real GNP per capita Growth rate of real GNP Imports to GDP ratio

S. Hoti and M. McAleer

Real GDP per capita Amortization over exportsGrowth rate of GDP per capita Savings over investment Growth rate of exports GDP growth rate of OECD countries High political violence Low political violence Assassinations Government crises Demonstrations Guerrilla warfare Purges Revolutions Riots Strikes Balkan’s (1992) political instability index

The economic content of indicators of developing country creditworthiness

International monetary fund

Pooled data for over 60 developing countries for 1980– 1993

Outstanding debt to exports ratio Interest obligations due to exports ratio Principal obligations due to exports ratio Indicator for past IMF support and/or rescheduling Indicator for presence of signiﬁcant arrears Exports to GDP ratio Cumulated number of years since 1970 with IMF involvement Cumulated number of reschedulings since 1970 Dummy variable taking the value one after 1973 Dummy variable for regime 3 in year ðt 2 1Þ Dummy variable for regime 2 in year ðt 2 1Þ

Institutional Investor country risk ratings Euromoney country risk ratings Economist intelligence unit country risk ratings

Measures of external shocks: Terms of trade Three-month US Treasury bill rate Measures of external sector performance: Export growth Current account to GDP ratio International reserves to GDP ratio Debt dummies (to capture the differential treatment between high debt and low debt countries) Real exchange rate Measures of domestic economic performance: GDP growth

Country Risk Models: An Empirical Critique

Haque et al. (1996)

Supply of new loans (net new loans over exports) Limit on debt arrears (level of debt service obligations in arrears) Dummy variable for debt crisis (regime 2) Dummy variable for signiﬁcant arrears but no real ‘crisis’ (regime 3)

(continued)

47

48

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

S. Hoti and M. McAleer

Inﬂation dummies (to capture the differential treatment between high-inﬂation countries and low-inﬂation countries; proxy for quality of economic management) Measures of regional and structural characteristics (intercept dummies): Regional categories: Africa Asia Middle East Europe Western Hemisphere Export-orientation categories: Primary goods Fuel Manufactured goods Services and recipient of private transfers Diversiﬁed export base Financial classiﬁcation: Diverse borrower Ofﬁcial borrower Market borrower Lagged dependent variable

A model-based estimation of probability of default in sovereign credit markets

Journal of Development Economics

Kharas (1984)

The long-run creditworthiness of developing countries: theory and practice

Quarterly Journal of Economics

Pooled data for 33 Probability of debt Long-run multiplier (measure of persistence of developing default by a country shocks to the rate of growth of GDP and proxy for countries for the long-run output response to the ‘bad-luck’ of a 1970– 1988 string of negative shocks to output) Standard deviation of the residual of AR(1) process of GDP (proxy for the likelihood of negative shocks to GDP) Ratio of the sum of exports and imports over GDP (measure of a country’s economic openness and proxy for penalties uncaptured by lenders) Ratio of ofﬁcial exchange rate over black market exchange rate (measure of a country’s ﬁnancial openness and proxy for penalties uncaptured by lenders) Foreign reserves LIBOR rate Pooled data for 43 Probability of debt Debt service over GDP (proxy for capital stocks) developing rescheduling by a ratio countries for country in a given Capital ﬂows net of amortization over GDP ratio 1965– 1976 year Population over GDP ratio (inverse of per capita GDP) Investment over GDP ratio Proxy variables for expected capital ﬂows (based on procedures used to model expectations): Current capital ﬂows 1-year lagged capital ﬂows

Country Risk Models: An Empirical Critique

HernandezTrillo (1995)

(continued)

49

50

Appendix 2.1. Continued Topic

Journal/Source

Data

Dependent Variables

Kugler (1984)

Economic indicators and debt reschedulings 1977– 1981: empirical results from a logit model

Rivista Internatiozionale di Scienze Economiche e Commerciali

Pooled data for 47 developing countries for 1977– 1981

Probability of debt rescheduling by a borrowing country Probability that a country is forced to reschedule its external debt in the present year Probability that a country has to reschedule its external debt in the future

Kutty (1990)

Logistic regression Applied and probability of Economics default of developing countries debt

Explanatory Variables Debt service ratio Reserves to import ratio Debt to export ratio Short-term bank credits to export ratio Inﬂation rate Growth of money supply

Probability of debt Debt service ratio Pooled data for 79 developing default by a country Growth rate of exports Growth rate of imports countries for GDP growth rate (proxy for economic growth) 1964– 1982 Ratio of net resource transfer to GDP Ratio of debt amortization to total debt Ratio of total outstanding to GDP Ratio of external debt to international reserves Ratio of international reserves to imports Interest rate on private loans Interest rate on all debts Inﬂation rate of change

S. Hoti and M. McAleer

Author(s)

Lanoie and Lemarbre (1996)

Three approaches to predict the timing and quantity of LDC debt rescheduling

Applied Economics

Probability of debt rescheduling (equals one if a country reschedules its debt, 0 otherwise) Average value of debt rescheduling weighted by the total debt

Traditional macroeconomic variables: Debt service payments over exports ratio Debt amortization over total debt ratio Imports over reserves ratio Inﬂation rate Total debt over exports ratio GDP per capita Use of IMF credits over total IMF reserves ratio Structural variable (average for the period 1984– 1988 inclusively): Current account over GDP ratio Difference between GDP and GNP growth rates Investments over GDP ratio (index of capital productivity) Ratio of the sum of imports and exports over the sum of GDP and imports (index of the country’s outwardness orientation) Natural log of population Ratio of the share of the richest quintile over the share of the poorest quintile (index capturing the structure of income distribution) Agriculture share in GDP (%) Terms of trade Share of export revenues from raw materials (%) Savings over GDP ratio GDP per capita Total debt relative to GDP

Country Risk Models: An Empirical Critique

Cross section data for 93 developing countries Data on debt rescheduling for 1989 and 1990 Data on economic variables for 1984– 1988

(continued)

51

52

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

S. Hoti and M. McAleer

Balance sheet variables: Total bank borrowing relative to total bank deposits Short-term bank debt relative to total bank borrowing Medium-term plus long-term bank debt relative to short-term bank debt Undisbursed credit commitments relative to total bank borrowing Unallocated credits relative to total borrowing Medium and long-term bank debt relative to total bank borrowing Long-term bank debt relative to total bank borrowing Total foreign reserves, excluding gold, divided by IMF quota Use of IMF credits relative to IMF quota Weighted average of the grace periods of reschedulings Weighed average of the maturities of reschedulings Mills ratio

A behavioural approach to sovereign debt reschedulings

Applied Economics Letters

Cross section data Average amount of for 88 developing sovereign countries rescheduled debt Data on rescheduled debt for 1985– 1988 Data on economic variables for 1975– 1984

Maximum value of annual income during the 10 years prior to 1984 minus income for 1984 Income in 1984 minus the minimum value of income during the 5 years prior to 1984 Change in foreign reserves (1984) Change in total public debt held by foreigners (1984) Maximum value of private consumption in the 10 years prior to 1984 minus private consumption for 1984 Maximum value of government consumption during the 10 years prior to 1984 minus government consumption for 1984 Private consumption in 1984 minus the minimum value of private consumption in the 5 years prior to 1984 Government consumption in 1984 minus the minimum value of government consumption during the 5 years prior to 1984

Lee (1991a)

Ability and willingness to service debt as explanation for commercial and ofﬁcial rescheduling cases

Journal of Banking and Finance

Pooled data for 75 less-developed countries for 1970– 1985

Interest rate on international lending Growth rate of per capita GDP Ratio of total foreign debt to GNP Growth rate of industrialized countries’ GNP Variability of changes in per capita GDP (proxy for perceived cost or penalty imposed by creditors when a borrowing country defaults on its debt service obligations)

Probability of commercial debt rescheduling by a borrowing country Probability of ofﬁcial debt rescheduling by a borrowing country

53

(continued)

Country Risk Models: An Empirical Critique

Lee and Powell (1995)

Author(s)

Topic

Journal/Source

Data

Dependent Variables

54

Appendix 2.1. Continued Explanatory Variables

Probability of com- Ratio of government debt held domestically to mercial and ofﬁcial GDP (proxy for government ﬁscal policy) debt rescheduling by a borrowing country Lee (1991b)

Journal of International Money and Finance

Pooled data for 74 developing countries for 1970– 1987

Probability of debt rescheduling (equals one when rescheduling occurs, 0 otherwise) Probability of partial reneging given that a borrower has decided to reschedule (equals one when partial reneging occurs, 0 if pure rescheduling occurs) (partial reneging and pure rescheduling refer to those rescheduling cases in which a borrower did and did not lower its

Interest rate on international lending: Growth rate of GDP per capita Ratio of total foreign debt over GNP Growth rate of industrialized countries’ GNP (lagged) Variability of changes in GDP per capita (measured as the standard error of the regression of changes in per capita GDP on a constant and a time trend) Ratio of government debt held domestically over GDP Dummy variable for highly indebted countries (proxy for the bargaining power of the borrowers) Dummy variable for major borrowers (proxy for the bargaining power of the borrowers)

S. Hoti and M. McAleer

Using terms of rescheduling as proxy for partial reneging on LDC’s debt in a test of willingness-to-pay model

Lee (1993a)

Relative Journal of importance of International political instability Business and economic variables on perceived country creditworthiness

Cross-sectional data for 29 developing countries Data on country risk ratings and economic variables for 1986 Data on political variables for 1967– 1986

Institutional Investor country risk ratings (logistically transformed)

Economic variables: Total foreign debt over GDP Growth rate of GDP per capita Government debt held domestically over GDP (proxy for government ﬁscal policy) Political variables (same as in Brewer and Rivoli, 1990): Recent political conditions: Number of changes in the head of government between 1982– 1986 (proxy for governmental regime stability) Political rights scores for 1986, (proxy for political legitimacy) Armed conﬂict in progress at the end of 1986 (proxy for armed conﬂicts)

55

(continued)

Country Risk Models: An Empirical Critique

debt service obligations, respectively) Trichotomous variable of debt rescheduling (equals 0 if no rescheduling, one if a pure rescheduling occurs, and two if a partial reneging occurs)

56

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

Lee (1993b)

Are the credit ratings assigned by bankers based on the willingness of LDC borrowers to repay?

Journal of Development Economics

Pooled data for 40 developing countries for 1979– 1987

Probability of debt rescheduling Institutional Investor country risk ratings (logistically transformed) Institutional Investor country risk ratings (logistically transformed)

Institutional Investor country risk ratings Time dummy (equals one if observation is from 1983– 1987, 0 otherwise) Probability of debt rescheduling (equals one when a rescheduling occurs in year t; 0 otherwise) First, second, and third lag of the probability of debt rescheduling Time dummy (equals one if observation is from 1983– 1987, 0 otherwise) Ratio of total foreign debt over exports Growth rate of GDP per capita Interest rate on international lending Growth rate of industrialized countries’ GNP Variability of changes in GDP per capita Average rate of inﬂation over a 3-years period (proxy for government domestic policies) Regional dummies:

S. Hoti and M. McAleer

Long term political conditions: Number of changes in the head of government between 1967 –1982 (proxy for governmental regime stability) Political rights mean score for 1975– 1986 (proxy for political legitimacy) The number of years conﬂict in progress as of the end of 1986 (proxy for armed conﬂicts)

East Asia and the Paciﬁc Latin America and the Caribbean North Africa and Middle East More developed Mediterranean Dummy for highly indebted countries Dummy for major borrowing countries Using country Economics balance of sheet Letters data to predict debt rescheduling

Pooled data for 27 developing countries for 1977– 1981 Pooled (with semi-annual) data for 59 developing countries for 1977(2)– 1985(2) Seasonally adjusted data (?)Seasonally adjusted data (?)

Probability of band and ofﬁcial debt rescheduling by a borrowing country

Traditional variables: Growth of export volumes Country balance sheet variables: Long-term to total borrowing ratio Proportion of each country’s debt relative to total bank lending for the sample Foreign exchange reserves divided by the IMF quota Proportion of short-term band debt to the total bank of a country Total bank borrowing relative to bank deposits Unallocated credit divided by total lending to a country Undisbursed credit commitments divided by total bank lending to that country Global attitude variables (proxy for ‘herd’ or ‘global’ inﬂuences): Number of current reschedulings Value of (current) reschedulings Weighted average of the maturities of (current) reschedulings

57

(continued)

Country Risk Models: An Empirical Critique

Lloyd-Ellis et al. (1989)

58

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

Lloyd-Ellis et al. (1990)

Predicting the quantity of LDC debt rescheduling

Economics Letters

Pooled data for 27 (larger debtor) developing countries for 1977– 1981 Pooled (with semi-annual) data for 59 developing countries for 1977(2) – 1985(2) Seasonally adjusted data (?)

Probability of debt rescheduling (equals one if debt rescheduling occurs, 0 otherwise) Quantity of debt rescheduling

Traditional macro variables: Growth rate of exports Balance sheet variables: Total bank borrowing over total bank deposits ratio Short-term debt over total debt ratio Foreign exchange reserves over IMF quota ratio Long-term borrowing over total bank debt ratio Proportion of medium-term to total bank debt Undisbursed credit commitments over total bank lending ratio Unlocated credit over total lending ratio Global’ or ‘herd’ variables (capturing the changing global attitudes to rescheduling and the opportunity cost of not rescheduling): Number of reschedulings Value of reschedulings Average grace period of new rescheduling

S. Hoti and M. McAleer

Weighted average of the grace periods of (current) reschedulings Dummy variables to reﬂect the changed ﬁnancial position of a country two periods following a rescheduling

Mascarenhas and Sand (1989)

Combination of forecasts in the international context: predicting debt reschedulings

Journal of International Business Studies

Pooled data for 40 developing countries for 1980– 1984

Discriminant score of whether a country belongs to a rescheduling group or nonrescheduling group

Objective economic indicators: Inﬂation rate Growth rate of real GDP Ratio of total external debt to exports Ratio of external debt service payments to exports Bankers’ subjective judgement: Institutional Investor country risk rating Objective economic indicators Bankers’ subjective judgement residuals (obtained when regressing the objective indicators against the subjective country risk rating to reduce overlapping explanations!)

59

(continued)

Country Risk Models: An Empirical Critique

Maturity of new rescheduling Dummy variable (equals one in the two periods following a rescheduling, 0 otherwise) (captures the changed ﬁnancial position of a country after a rescheduling) Other variables: Mill’s ratio (to allow for the sample selection bias introduced through only using non-zero values) Standard deviation of the error term when regressing the quantity of rescheduling on the above explanatory variables) Correlation coefﬁcient between the error term of the above regression equation and the error term of the selection probit equation

60

Appendix 2.1. Continued Topic

Moon and Stotsky (1993)

Testing the differences between the determinants of Moody’s and Standard and Poor’s ratings: an application of smooth simulated maximum likelihood estimation

Journal/Source Journal of Applied Econometrics

Data

Dependent Variables

Explanatory Variables

Cross-sectional data for 892 municipalities in USA Data on municipality credit ratings for 1982 Data on ﬁscal variables for 1980– 1981 Data on all other variables for 1970– 1980

A municipality’s propensity to obtain Moody’s risk ratings Moody’s municipality risk ratings A municipality’s propensity to obtain S&P’s risk ratings S&P’s municipality risk ratings

Median housing value (proxy for local property tax) The proportion of housing units that were built before 1940 The proportion of housing units that were built after 1970 The proportions of housing units that were owneroccupied Per capita income in 1979 (proxy for local property tax) The percentage change in population from 1970 to 1980 Population density The proportion of the population that is non-white Total debt Debt per capita Ratio of debt to income Ratio of intergovernmental revenues to general revenues Dummy for the council-manager form of government (captures administrative effects) Dummy for the commission form of government (captures administrative effects) Dummy for municipalities in mid-western states (captures regional effects)

S. Hoti and M. McAleer

Author(s)

Morgan (1986)

A new look at debt rescheduling indicators and models

Journal of International Business Studies

Pooled data on 30 Probability of developing (larrescheduling by a gest debtor) borrowing country countries for 1975– 1982

Demand side variables: Ratio of exports to imports (proxy for the current account balance) Ratio of international reserves to imports Supply side variables: Total debt to exports ratio Short-term debt to imports ratio Principal payments to total debt ratio Current debt service ratio Exogenous shock variables: Interest rate sensitivity indicator Ratio of total bank lending to the non-oil developing countries over the total current account deﬁcits of the non-oil developing countries Real GDP growth rate

Country Risk Models: An Empirical Critique

Dummy for municipalities in southern states (captures regional effects) Dummy for municipalities in western states (captures regional effects) Cities with 100,000 to 500,000 people (captures the population size effects) Cities with more than 500,000 people (captures the population size effects)

(continued)

61

Author(s)

Topic

Odedokun (1995)

Analysis of probability of external debt rescheduling in sub-Saharan Africa

Journal/Source

Data

Dependent Variables

Scottish Journal Pooled data for 39 Probability of debt of Political African countries rescheduling by a Economy (including 35 sub- country Saharan countries) for 1980– 1990

62

Appendix 2.1. Continued Explanatory Variables

S. Hoti and M. McAleer

Existing debt burden or overhang: Debt over GNP ratio Debt over exports ratio Debt service payment ratios: Debt service payments over GNP ratio Debt service payments over exports ratio Proportion of debt attracting variable interest rate: Variable interest rate debt over total debt ratio Foreign reserves position: Foreign reserves over debt ratio (ﬁrst proxy for foreign reserves position) Foreign reserves over imports ratio (second proxy for foreign reserves position) Foreign reserves over GNP ratio (third proxy for foreign reserves position) Foreign capital ﬂows: Commercial capital inﬂows over GNP ratio (proxy for foreign capital inﬂows) Terms of trade improvements: Term of trade Export unit value Import unit value External trade ratios: Real export growth

Oetzel et al. (2001)

Country risk measures: how risky are they?

Journal of Pooled (with World Business monthly, semiannual and annual) data for 17 developed and developing countries for 1980– 1998

10% depreciation in the value of the national currency per month (proxy for overall country risk)

Institutional Investor risk ratings (semi-annual) Lagged Institutional Investor country risk ratings Euromoney country risk ratings (semi-annual) Lagged Euromoney country risk ratings ICRG(annual): Repudiation ratings

Country Risk Models: An Empirical Critique

Imports over GDP ratio (proxy for dependence of a country of the international economy) Trade deﬁcit over GDP ratio Position with IMF: Outstanding IMF credit over imports ratio Exchange rate movements: Real exchange rate index (with upward movement representing real depreciation of domestic currency) Rate of nominal devaluation of domestic currency Domestic economic indicators: Investment spending over GDP ratio Growth of real GDP Inﬂation rate Per capita income Agriculture output over GDP ratio

(continued)

63

64

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Oral et al. (1992)

An estimation model for country risk rating

International Journal of Forecasting

Institutional Cross-sectional Investor country data on 70 risk ratings developed and developing countries for 1982 and 1987

Lagged repudiation ratings Expropriation ratings Lagged expropriation ratings Corruption ratings Lagged corruption ratings Rule of law ratings Lagged rule of law ratings Bureaucratic quality ratings Lagged bureaucratic quality ratings Political risk services (monthly): Political turmoil risk ratings Lagged political turmoil risk ratings Finance transfer risk ratings Lagged ﬁnance transfer risk ratings Direct investment risk ratings Lagged direct investment risk ratings Export market risk ratings Lagged export market risk ratings Reserves to imports ratio Net foreign debt to exports ratio GNP per capita Current account balance to GNP ratio Investment to GNP ratio Export variability

S. Hoti and M. McAleer

40% depreciation in the value of the national currency per month (proxy for overall country risk) 10% appreciation in the value of the national currency per month (proxy for overall country risk)

Explanatory Variables

Export growth rate Political instability indicator Country group dummy The determinants of debt rescheduling: the case of Latin America

RahnamaMoghadam (1995)

Debt rescheduling The Journal of in less-developed Developing countries: world or Areas regional crisis?

Southern Economic Journal

Pooled data for 16 Latin American countries for 1980– 1987

Probability of debt rescheduling by a country

Ratio of total debt service to exports of goods and services (known as debt service ratio) Ratio of total debt service to GNP (known as the burden of debt relative to a country’s income) Ratio of total international reserves to debt outstanding and disbursed Ratio of public loans with variable interest rates to total debt

Probability of debt Pooled data for 91 developing rescheduling by a country countries for 1980– 1989

Economic and ﬁnancial predictor variables: Total international reserves over outstanding and disbursed debt ratio Total debt service over GNP ratio Total debt service over exports ratio Public loans with variable interest rates as a percentage of total outstanding and disbursed debt Public sector and political predictor variables: Ratio of total central government expenditures over GNP Ratio of total military expenditures over total central government expenditures

Country Risk Models: An Empirical Critique

RahnamaMoghadam et al. (1991)

(continued)

65

66

Appendix 2.1. Continued Author(s)

Topic

Journal/Source

Data

Dependent Variables

Explanatory Variables

The determinants Global Finance Journal of secondary market prices for developing country loans: the impact of country risk

Secondary market Cross-sectional Euromoney score on debt indicator, Euromoney data for 27 devel- price of a country’s score on debt in default or rescheduled, foreign debt oping countries Euromoney country risk ratings for 1992– 1994

Rivoli and Brewer (1997)

Political instability Global Finance and country risk Journal

Pooled data for 80 Probability of debt rescheduling by a developing country countries for 1980– 1990

Economic variables: Debt service ratio, Import coverage ratio (in months), Ratio of external indebtedness to GNP, Ratio of scheduled debt service to external debt, Ratio of the current account balance to GNP Short-term political variables: Number of changes in the head of the government over the preceding 5 years, Political rights score (based on voting and other civil rights) for the current year, Number of changes in the governing group over the preceding 5 years, Dummy for the presence and absence of armed conﬂicts in the current year

S. Hoti and M. McAleer

Ramcharran (1999)

Schmidt (1984)

Early warning of debt rescheduling

Journal of Banking and Finance

Cross-sectional data for 52 developing countries for 1974– 1978

Discriminant score of whether a country belongs to rescheduling group or non-rescheduling group

Debt service to outstanding debt (disbursed and undisbursed), Interest payments to average outstanding debt, Debt from suppliers to total debt, Annual growth of outstanding debt (including undisbursed), Annual growth of outstanding debt from suppliers, Outstanding debt to exports, Outstanding debt to GD, Interest payments to GDP, GDP per capita, Total reserves minus gold to imports, Exports to GNP ratio

Country Risk Models: An Empirical Critique

Long-term political variables: Number of changes in the head of the government over the preceding 20 years, Average of political rights score (based on voting and other civil rights) for the preceding 12 years, Number of changes in the governing group over the preceding 20 years, Number of years armed conﬂicts have been in progress as of the end of the current year

(continued)

67

68

Author(s)

Topic

Journal/Source

Scholtens (1999)

On the The Journal of comovement of Fixed Income bond yield spreads and country risk ratings

Somerville and Tafﬂer (1995)

Banker judgement vs. formal forecasting models: the case of country risk assessment

Journal of Banking and Finance

Data

Dependent Variables

Explanatory Variables

Pooled (with semi-annual) data for 25 developed and developing countries for 1993(1)– 1996(2)

Yield spreads of international bonds (yield of the international bonds minus US Treasury Bond yield)

Remaining life of a bond Country risk indicator considered for comparison: Institutional Investor country risk ratings

Pooled data for 54 developing countries, data for Institutional Investor country risk ratings and for

Institutional Investor country risk ratings (in each year), country creditworthiness

Country creditworthiness (in the following year), Net assets (end-period foreign exchange reserves, SDRs and reserve position with IMF, less total public and private disbursed short-, medium- and long-term external debt) over GDP ratio, Inﬂation rate,

S. Hoti and M. McAleer

Appendix 2.1. Continued

Country risk: a model for predicting debt servicing problems in developing countries

Journal of the Royal Statistical Society, Series A

(a binary variable GDP growth, which takes the Interest payments over debt service ratio, value 1 if arrears Gross ﬁxed capital formation over GDP ratio of external debtservice emerge for a country in a given year, or 0 otherwise)

Pooled data for 95 developing countries for 1967– 1978

Discriminant score of whether a country belongs to rescheduling country-year group or non-rescheduling country-year group

Loan commitments per capita, Debt to exports ratio, Average rate of inﬂation, Domestic credit to GDP ratio Country risk indicator considered for comparison: Institutional Investor country risk ratings (proxy for banker judgement)

Country Risk Models: An Empirical Critique

Tafﬂer and Abassi (1984)

the explanatory variables in the multivariate models for 1979– 1989, data for the binary dependent variable for 1980– 1990

69

Author(s)

Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Descriptive Statistics

Diagnostics

70

Appendix 2.2. Analysis of models Empirical Findings

None

None

None

Mean

None

Forecasting oncoming payment difﬁculties which major debtor developing countries have been experiencing with increasing frequency is crucial. This paper compiled an index as the weighted average of a number of variables, which have consistently preceded most payment interruption situations. The key factors include erosion in international reserves, a substantial faster growth in external debt compared to exports, acceleration in the inﬂation rate, a deterioration in export performance, and adverse internal political developments

Backer (1992)

None

Logit

ML

Error probability of None the Wald-x2 and x2 ; error ratio

Unlike the results in Lloyd-Ellis et al. (1989), only three balance sheet variables and only one herd variable are signiﬁcant at this level. The inﬂuence of the ﬁnancial variables is continuous whereas traditional macro variables vary in their importance for explaining reschedulings. The results suggest that the traditional ratio variables jointly have high explanatory variables and should therefore be omitted as regressors. In contrast to the balance sheet variables, whose inﬂuence is independent of the prediction lag, traditional macro variables have a lagspeciﬁc impact on the rescheduling probability. Global events have spillover effects on debtor countries’ future productivity and ability to pay. A delay of such impacts could explain the rising importance of these variables when lags are increased. Minimum error ratios of 10.6 and 11.2% occur in predictions with a horizon of 12 and 24 months, respectively. The predictive abilities of the logit models have also been tested by a pairwise comparison of observations

S. Hoti and M. McAleer

Abdullah (1985)

Political events

Probit

ML

t-statistic, x2

None

Quantiﬁed proxies of political events should be included in the assessment of overall country risk exposure. There is an inverse relationship between the rescheduling probabilities for a given country and its level of democracy, and a direct relationship between therescheduling probabilities and the level of political instability

de Bondt and Winder (1996)

Worldwide economic activity

Probit

ML

t-statistic, maximum log-likelihood, R2 ; mean, standard deviation, correlation coefﬁcients, Spearman rank correlation coefﬁcients

None

The model yields a country risk indicator, which incorporates the impact of all macroeconomic variables and policy-related and global aggregates. Comparison of the country risk indicator with Euromoney and Institutional Investor ratings shows that the indicator has the highest standard deviation. For an early warning system, this is an advantage because the indicator provides clear signals. The country risk indicator shows a rank correlation of 0.8 with the other country risk ratings

Brewer and Rivoli (1990)

Governmental regime stability, political legitimacy, armed conﬂicts

Logit

OLS

t-statistic, F-statistic, R2

None

Political instability cannot be measured by a single variable. It is feasible to develop quantitative measures of the various dimensions of political instability to be included along with economic variables in models of country risk

Burton and Inoue (1985)

None

None

None

Correlation coefﬁcients

None

The variables reviewed in this study are common to most of the country risk models developed by major international banks. These variables are drawn from bankers’ judgement and historical experience and theory and empirical analysis. Sadly, despite a clear recognition of political risk, the quantiﬁcation of political instability is neglected in bank models of country risk

Country Risk Models: An Empirical Critique

Balkan (1992)

(continued)

71

72

Appendix 2.2. Continued Method of Estimation

Political stability, minimum income or development level, credit reputation, consolidated deﬁcits of federal, state, local and quasi-public sectors, net foreign assets, debt-service costs, maturity of external liabilities, anticipation

Linear single equations, log –linear single equations

Chattopadhyay (1997)

None

Citron and Nickelsburg (1987)

Political instability

Recognition of Omitted Explanatory Variables

Cantor and Packer (1996)

Descriptive Statistics

Diagnostics

Empirical Findings

OLS, OLS and WLS

t-statistic, SE, mean change, median change, Zstatistic, adjusted R2 ,

Use speciﬁcation of the logarithm of yields against ratings and WLS for heteroscedasticity

S&P and Moody’s country risk ratings summarize and supplement the information contained in macroeconomic indicators, and are strongly correlated with market-determined credit spreads. Event study analysis shows that the rating agencies’ opinions independently affect market spreads. Although the agencies’ ratings have a largely predictable component, they also appear to provide the market with information about non-investment-grade countries that goes beyond that available in public data. Given the difﬁculty in measuring country risk, especially for below-investment-grade borrowers, country risk ratings appear to be valued by the market in pricing issues

Logit, artiﬁcial neural network (ANN) model

ML, backward error propagation method, counter propagation method

Mean, standard deviation

None

The logit model was able to correctly predict country risk ratings in approximately 63% of the cases. The results of the neural network models are encouraging. The predictive accuracy rate for these models ranges from 51 to 61%. Further investigation, in terms of network conﬁgurations and the measure of the dependent variable, is required to examine the capability of neural networks to predict country risk ratings

Logit

ML

SE

None

The estimated coefﬁcients of international reserves and political instability indicator are highly signiﬁcant. A reduction in international reserves is an indicator of a debt crisis. The political instability indicator is positively

S. Hoti and M. McAleer

Model

Author(s)

related to default. This implies that new governments tend to be weaker and, therefore, less likely to meet debt payments on schedule None

Discriminant model, logit, probit, ANN model

Discriminant analysis, ML, ML, back-propagation algorithm

Type I error, Type II error

None

The ANN approach was able to classify countries into one of two mutually exclusive groups with a high degree of accuracy. It largely outperforms conventional multivariate statistical procedures employed for this purpose. While logit and probit generally outperform the discriminant model. However, this does not mean that ANN models are the solution for all classiﬁcation problems. ANN will only identify relationships among variables where such relationships actually exist. They are worthy of consideration by researchers

Cosset and Roy (1991)

None

Logit

OLS (?)

t-statistic, R2

None

The two country risk measures are highly correlated and can be replicated with a few available economic statistics. This implies that Euromoney and Institutional Investor strongly agree on the creditworthiness of the assessed countries and that both magazines’ ratings provide market participants with little informational value

Doumpos and Zopounidis (2001)

None

Multi-group hierarchical discrimination (MHDIS) model, linear discriminant function

Regression-based technique, multiple discriminant analysis (MDA)

t-statistic, standard deviation

None

The MHDIS method develops additive-utility models that can be used for classiﬁcation purposes in country risk assessment. The overall classiﬁcation accuracy of this country risk evaluation model is 99.3%. With regard to the training sample, the overall classiﬁcation accuracy of the MHDIS is signiﬁcantly higher than for MDA. With regard to the validation sample and in terms of the average overall classiﬁcation accuracy, MHDIS still performs better than MDA, although the difference between the two models is smaller as compared with the training sample.

Country Risk Models: An Empirical Critique

Cooper (1999)

(continued)

73

74

Appendix 2.2. Continued Author(s)

Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Descriptive Statistics

Diagnostics

Empirical Findings

Easton and Rockerbie (1999)

None

Logit, tobit, probit, linear single equations

ML, ML, ML, OLS t-statistic, Cragg –Uhler R2 ; R2 ; F-statistic, Mill’s ratio

White’s heteroschedasticity-consistent covariance matrix estimator

The optimal interest spread over LIBOR was derived and estimated using default probabilities obtained from a logit model (unscaled probabilities) and a tobit model (scaled probabilities). The tobit probabilities slightly outperform the logit probabilities, indicating that incorporating default as a matter of degree has an important role to play in models of lending to LDCs. The post-1981 dummy variable was negative and signiﬁcant, suggesting a decrease in the average weighted default probability after 1981. A competing model using rescheduling, rather than accumulated arrears, as the condition for default was developed and tested with poor results. Spreads were either weakly positively related to default risk or not at all related

Eaton and Gersovitz (1980)

None

Five-equation system

ML

None

The paper addresses the issues of the relationship between LDC borrowing and reserve demand, and the likelihood of widespread default. Debt is a substitute for reserves in as a transaction medium. The maximum amount of credit which private lenders are willing to extend depends positively on export variability and per capita income and negatively on population.

SE, maximum likelihood estimate divided by the standard error, correlation coefﬁcients

S. Hoti and M. McAleer

The major problem in both models is to identify the countries belonging to the upper-middle income economies group. However, most of these classiﬁcations are assigned to the group of lower middle income economies

Generally, borrowers are constrained in the amount they can borrow by credit ceilings. The unconstrained countries are characterized by strong export performance. The export performances of individual countries are largely uncorrelated, suggesting that defaults are likely to occur independently rather than in groups. To the extent that total developing countries exports move together, the large borrowers’ exports are correlated with the overall developing countries export experience None

Three-equation system

ML

Maximum likelihood estimate divided by the standard error, maximum log-likelihood, error variance

None

A crucial characteristic of the poor country-borrowing incident is the absence of explicit penalties for nonpayment. Borrowers who repudiate their debt face future exclusion from capital markets. Under the assumption that this exclusion is permanent, lenders will establish a credit ceiling above which they will be unwilling to increase loans. This theoretical model relates both the credit ceiling and the demand for credit to a set of observable borrower characteristics. Empirical results coincide quite closely with the structure of the theory. The variability of export revenue increases both the demand for and supply of debt

Edwards (1984)

None

Random-effect error component equations

Fuller –Batesse technique

t-statistic, mean square error

None

Bank lending behaviour has considered some of the economic characteristics of countries when determining the spread they charge. Unlike previous ﬁndings, there is a signiﬁcant positive relationship between the spread over LIBOR and the debt to output ratio. This relationship suggests that there are externalities in the process of LDC borrowing. These externalities could be dealt with by imposing an optimal external borrowing tax in these countries.

Country Risk Models: An Empirical Critique

Eaton and Gersovitz (1981)

(continued)

75

Author(s)

Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Descriptive Statistics

Diagnostics

76

Appendix 2.2. Continued Empirical Findings

Erb et al. (1996)

None

Linear single equations, log– linear single equations

OLS, OLS

t-statistic, R2

None

Feder and Just (1977)

Non-compressible imports

Logit

ML

Maximum loglikelihood, t-statistic

None

For portfolios based on changes in the risk ratings, economic and ﬁnancial risk can predict the cross-section of stock returns, especially in developed markets. Changes in political risk also have some explanatory power in developing equity markets but not in developed markets. Country risk ratings, particularly the economic risk variables, are correlated with fundamental valuation attributes. The results shed light on the information that determines the fundamental valuation measures All economic variables, except GDP growth, are signiﬁcantly related to debt servicing capacity. Exports are important in both a static sense (the debt service ratio) and a dynamic sense (export growth). The signiﬁcance of export growth coefﬁcient increases appreciably when GDP growth is removed (whether or not amortization to debt ratio is considered). Comparing the results with and without the amortization to debt ratio, the estimates of the other coefﬁcients are approximately of the same magnitude. Their signiﬁcance is generally improved when amortization is not included

S. Hoti and M. McAleer

The coefﬁcients of reserves to GNP ratio, gross investment to GDP ratio, and current account ratio are not always signiﬁcant and have the correct signs, except the coefﬁcient of the current account ratio. The coefﬁcients of the remaining variables are insigniﬁcant

Political factors

Logit

First-order approxi- Maximum loglikelihood, mation, secondt-statistic order approximation

None

Each of the estimated coefﬁcients in the ﬁrst-order approximation model has the expected sign and is signiﬁcant at the 10% level. In the second-order approximation model, the results hold even for the three additional second-order terms. When regional dummy variables are included, the relative GNP per capita becomes statistically insigniﬁcant. This implies that per capita income and the regional dummy variables reﬂect essentially the same underlying factors, at least within the sample period

Feder and Uy (1985)

None

Logit

OLS

t-statistic, R2

Chow test

All the explanatory variables are signiﬁcant in explaining country creditworthiness and have the expected signs. The results change when time dynamics are taken into account. For 1981(1) –1983(1) period there is a decline in the weight attached to debt servicing difﬁculties and an increase in the weight attached to export vulnerability. Simulation results highlight the importance of export promotion and expansion in preventing deterioration in the creditworthiness of developing countries. Successful expansion of exports depends on both internal policies and external circumstances that are beyond the control of the developing countries

Frank and Cline (1971)

None

Linear discriminant function (equal and unequal covariance) Quadratic discriminant function

Minimax approach (iterative process) Bayesian approach

t-statistic

None

It is possible to obtain a very high prediction rate using only three factors, the debt service ratio, the average maturity of debt, and imports to reserves ratio. The best predictions about debt servicing capacity are obtained when it is assumed that covariance matrices are unequal. Experiments with discriminant analysis show a very high prediction rate can be obtained using only two factors, the debt service ratio and the average maturity of debt

Country Risk Models: An Empirical Critique

Feder, et al. (1981)

(continued)

77

78

Appendix 2.2. Continued Author(s)

Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Descriptive Statistics

Diagnostics

Empirical Findings

None

Probit

ML

t-statistic

None

None of the political variables included in the model as explanatory variables is signiﬁcant with the ‘correct’ sign. This implies that changes in the political situation are already reﬂected in economic aggregates. This suggests that the inﬂuence of political factors is discounted in macroeconomic variables in the model

Hajivassiliou (1987)

None

Threeequation system with ﬁxed effects, nested trinomial logit, twoequation system, threeequation system with random effects

ML, ML, ML, ML

t-statistic, standard deviation, correlation coefﬁcients, maximum log-likelihood, l (dissimilarity parameter), x2

Hajivassiliou test for exogeneity, LM test, LR test, Wald test, Haussman test

The ﬁndings of the three-regime model (excess supply, moderate level of excess demand, and arrears limit becomes binding) conﬁrm the importance of creditworthiness indicators on the supply of funds and on limits on ‘acceptable’ levels of arrears. The stochastic shocks are found to arise primarily from the demand side. Substantial differences were observed in the tworegime (excess supply, excess demand) model when classifying as credit-constrained an economy that asks to reschedule its debt obligations, requests/accepts IMF involvement, or lets its obligations go into arrears. Weak evidence is found for the claimed glut of ‘petrodollars’ after the 1973 oil shock leading to higher levels of international lending. The hypothesis that liquidity problems induce external debt crises even for overall solvent borrowers was supported and the claimed erogeneity of the interest rate is not rejected. Withinsample predictions of debt crisis probabilities were used for testing the speciﬁcation of the models with favourable results.

S. Hoti and M. McAleer

de Haan et al. (1997)

Explicit allowance for country heterogeneity establishes a strong role for country-speciﬁc persistent unobservable effects, without eliminating the importance of a past repayment problems history Haque et al. (1996)

Quality of economic management

OLS

Pairwise correlation coefﬁcients, Kendall’s coefﬁcient of concordance, t-statistic, R2 ; adjusted R2

None

Due to the persistence in country risk ratings, the rebuilding of a country’ creditworthiness rating normally takes an extended period of time. However, for a country that has been experiencing a high rate of inﬂation, a sharp reduction in inﬂation would improve the country’s rating by moving it out of the highinﬂation grouping used by the rating agencies. Rebuilding the ratio of non-gold foreign exchange reserves to imports would also be an important step, as this variable has one of the highest elasticities in all the rating equations. An improvement in the country’s current account balance and a revival of growth would also help strengthen the country’s rating

Probit

ML

SE, slope of conditional mean function

White’s heteroschedastic-consistent covariance matrix to correct for heteroschedasticity

The factors that determine the probability of default must be drawn from a theoretical model. Economic liberalization reduces the probability of default and that the renegotiation process reduces the inherent DWL present in default. Unluckiness and persistence of a shock to the rate of growth of GDP are important in explaining such probability. It is shown that the spread depends on the degree of openness in the current account and the persistence of shocks to the rate of growth of the GDP

Country Risk Models: An Empirical Critique

HernandezLong-run output Trillo (1995) response to the ‘bad-luck’ of a string of negative shocks to output, the likelihood of negative shocks to GDP, penalties uncaptured by lenders

Log –linear single equations

(continued)

79

80

Appendix 2.2. Continued Model

Method of Estimation

Descriptive Statistics

Diagnostics

Recognition of Omitted Explanatory Variables

Kharas (1984)

Capital stocks, expected capital ﬂows

Probit

ML

t-statistic, likelihood ratio

Kugler (1984)

None

Logit

ML

SE, mean, ratio of None the difference of means to estimated standard error

Kutty (1990)

Economic growth

Logit

ML

t-statistic, x2

None

None

Empirical Findings

There is a minimum critical capital stock necessary to support current borrowing and existing debt-service obligations. A country whose capital stock exceeds this minimum is creditworthy, and not creditworthy otherwise. Investment shares, per capita income, net foreign capital inﬂows and debt service obligations are all signiﬁcant in explaining rescheduling and non-rescheduling cases. Evidence on the magnitude of the impact of these variables on creditworthiness is consistent with a view that savings rates and the marginal product of capital are important. The unconstrained probit estimators are consistent with theoretical information on two important parameters, namely the marginal propensity to invest out of income and net foreign inﬂows The results of the binary logit model show that debt to export ratio and the inﬂation rate are highly signiﬁcant indicators for rescheduling. The other indicators are signiﬁcant but have incorrect signs. The results of the three-choice logit model (rescheduling, pre-rescheduling and non-rescheduling) show that debt to export ratio and inﬂation rate remain signiﬁcant indicators for reschedulings, whereas debt service ratio and the money supply growth are signiﬁcant indicators for pre-reschedulings A country’s ability to service debt depends basically on the economic performance over a long period of time.

S. Hoti and M. McAleer

Author(s)

Lanoie and Lemarbre (1996)

None

Tobit (simultaneous probit and linear equations)

Heckman’s two-step method

Mean, standard deviation, adjusted R2 ; p-value, t-statistic, Type I error, Type II error

None

This paper examines the performance of three approaches to predict the timing and quantity of LDC debt rescheduling: the balance sheet approach, the traditional macroeconomic approach and the structural approach. Based on three evaluation criteria (minimized Type I error, highest percentage of right predictions and value of adjusted R2 ), results suggest that the balance sheet approach performs better. Thus, it could be useful for ﬁnancial analysts preoccupied by LDCs’ debt-servicing problems. However, further examination of the elements of the structural approach could still be useful for prediction purposes and be integrated to the balance sheet approach

Lee and Powell (1995)

None

Linear single equations

OLS

t-statistic, R2

White’s standard errors to correct for heteroschedasticity

A behavioural model of consumption demonstrates that the magnitude of potential sovereign debt reschedulings can be directly forecasted by the severity of preceding recession. Results indicate that reschedulings are foreshadowed by cuts in income as well as private and government consumption. These results could be used to develop a method for predicting the likelihood and magnitude of debt reschedulings well in advance

81

(continued)

Country Risk Models: An Empirical Critique

From a macroeconomic point of view, a country must have a measure of ﬂexibility in choosing between domestic inﬂation, economic growth and balance of payment deﬁcit. The use of external borrowing to ﬁnance excess current expenditure over current revenue should be viewed with caution. Export capacity should be increased, making available foreign exchange to service the debt. Developing countries should examine the inﬂation movements and current account indicators

82

Appendix 2.2. Continued Author(s)

Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Descriptive Statistics

Diagnostics

Perceived cost or Logit penalty imposed by creditors when a country defaults on its debt service obligations, government ﬁscal policy

OLS(?)

t-statistic, None p-value, x2 ; means, standard deviations, and t-statistics for the differences between the means of the explanatory variables, correlation coefﬁcients

Lee (1991b)

Bargaining power of the borrowers

ML, ML

t-statistic, p-value, x2

Sequentialresponse logit, unorderedresponse logit

None

As the value of a country’s total indebtedness relative to its income increases, or as the level of interest rates increases, borrowers face increased difﬁculty in meeting their debt service obligations. A higher economic growth rate enables a country to honour its debt service obligations. In this case, the borrower’s willingness to service its debt is higher, since the cost of default increases with an increase in national output level. Both the perceived cost of being excluded from credit markets and the growth rate of industrialized economies are signiﬁcant in commercial rescheduling cases but insigniﬁcant in ofﬁcial rescheduling cases. However, government domestic debt relative to GDP is signiﬁcant in ofﬁcial rescheduling cases, but not in commercial rescheduling cases. The results imply that ofﬁcial rescheduling is based on the economic performance of borrowers, the level of indebtedness and the level of interest rates. For commercial rescheduling cases, in addition to these three factors, access to international credit markets is a signiﬁcant factor. When debt is guaranteed by the creditors’ governments, creditors are less likely to impose penalties, and/or debtors do not perceive the possibility of being excluded from credit markets as seriously Explanatory variables are signiﬁcant in differentiating between rescheduling and non-rescheduling cases during the 1978 –1985 period. By separating rescheduling cases

S. Hoti and M. McAleer

Lee (1991a)

Empirical Findings

into subgroups based on the outcome of debt renegotiations, it is observed that a different set of explanatory variables inﬂuences the outcomes of debt renegotiations. It also appears that the outcome of debt renegotiations depends on the bargaining power of borrowers. Borrowers with a large absolute amount of foreign debt outstanding tend to reschedule their loans at more favourable rates Government ﬁscal policy, governmental regime stability, political legitimacy, armed conﬂicts

Logit

OLS(?)

t-statistic, R2 ; F-statistic, p-value

None

Both economic and the recent political situation of the country affect the creditworthiness of developing countries. Furthermore, economic variables have a greater impact on credit risk perceptions than recent political conditions of the borrowing countries. This implies that a country’s economic performance reﬂects longer-term political stability. None of the proxy variables for longer term political instability is statistically signiﬁcant

Lee (1993b)

Government domestic policies

Logit, linear single equations, two-way error components model

ML, OLS, ML(?)

t-statistic, p-value, x2 ; adjusted R2 ; F-statistic

None

Credit ratings provide a reasonable measure of a borrower’s creditworthiness. Lenders take into account the history of foreign debt reschedulings in assigning LDCs credit ratings. The set of explanatory variables selected from the willingness to pay model is signiﬁcant in explaining variations in the credit ratings. The inclusion in the model of dummy variables for geographical location of borrowers, degrees of indebtedness and absolute size of debt suggests that there may be a signiﬁcant group contagion in assigning credit ratings. It also appears that the rating criteria have changed since the onset of the crisis

Lloyd-Ellis et al. (1989)

‘Herd’ or ‘global’ inﬂuences

Logit

ML

t-statistic, R2

None

Annual data suggest that the balance-sheet variables are generally better at explaining rescheduling than the traditional variables used in previous research. The only traditional variable found to be statistically signiﬁcant is the rate of growth of exports.

Country Risk Models: An Empirical Critique

Lee (1993a)

(continued)

83

84

Appendix 2.2. Continued Author(s)

Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Descriptive Statistics

Diagnostics

Empirical Findings

Lloyd-Ellis et al. (1990)

None

Type II tobit (simultaneous probit and linear equations)

Heckman two-step method, FIML

t-statistic, R2 ; standard deviation, correlation coefﬁcient, Type I error, Type II error

None

The study estimated a Type II tobit model to explain the timing and quantity of LDC debt rescheduling. The annual results show that the traditional variables do not add greatly to the model once the balance sheet variables are present. Among the variables considered for the equation explaining the quantity of debt rescheduling, only the ratio of short-term debt to total bank debt is signiﬁcant. Mill’s ratio is not signiﬁcant but in the joint estimation, the correlation coefﬁcient of the errors is signiﬁcant. Quantity equations explain over 50% of the variation in the rescheduled quantities despite the fact that only one economic variable is present. An evaluation of the predictive capability of models involving qualitative variables depends crucially on (i) an examination of the Type I and Type II errors that occur at different probability levels, and (ii) a subjective evaluation of the relative

S. Hoti and M. McAleer

Four balance-sheet variables are signiﬁcant: the ratio of log-term to total borrowing, foreign exchange reserves to IMF quota, proportion of short-term band debt to the total bank debt of a country, total and total bank borrowing relative to bank deposits. However, the positive coefﬁcient of the ratio of short-term to total borrowing is not consistent with the ﬁnancial crisis story. Semi-annual data suggest that ﬁnancial variables are important, together with the global attitude variables. It is sensible to include BIS and IMF data on balance-sheet position of developing countries in logit models of debt rescheduling. Changing global circumstances and attitudes to rescheduling also play a role

costs of the two types of error. The semi-annual model includes variables to capture global attitudes to rescheduling together with balance sheet variables. If the sum of Type I and Type II errors is minimized, the error rates compare favourably with the literature Mascarenhas and Sand (1989)

None

Equal probability discriminant analysis Discriminant analysis Discriminant analysis Equal weighting and odds-matrix discriminant analysis

x2 ; Q-statistic

Use angular transformation of data to correct for non-normality

All three basic approaches perform better than a naı¨ve approach in forecasting debt reschedulings. Forecasting combination models provides improvements in the accuracy rate. The three basic forecasting approaches revealed statistically insigniﬁcant differences in accuracy rates, in general. The odds-matrix method shows the only statistically signiﬁcant improvement in accuracy over another non-naı¨ve approach, the single-forecast method based on subjective data. The odds-matrix method is the only method that incorporates information on the relative performance of forecasts in a prior period in assigning weights to future forecasts. Single-method forecasts should not be abandoned without considering their possible contribution in the forecastcombination context. The value of odds-matrix lies in its limited data requirements, sensitivity to the relative performance of the base forecasts, insensitivity to outlier forecasts, and limited assumptions about error terms. Forecast combinations are useful in the diverse sources of international business and can play an effective, theoretically based, politically neutral role in integrating multiple perspectives

Country Risk Models: An Empirical Critique

Naı¨ve approach model Single-method models: objective economic indicators, Bankers’ subjective judgement Raw data combination model: Combination of objective indicator and judgement data Forecast-combination models

(continued)

85

86

Appendix 2.2. Continued Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Moon and Stotsky (1993)

Local property tax

Four-equation system (with quadvariate latent variables and covariates)

Smooth simulated ML, optimal minimum distance estimation (OMDE)

Morgan (1986)

Current account balance

Logit, discrimi- ML, stepwise nant model method

Descriptive Statistics

Diagnostics

Empirical Findings

t-statistic, SE, maximum log-likelihood, correlation coefﬁcients; minimum x2

None

A quad-variate speciﬁcation is appropriate in analysing the determinants of Moody’s and S&P’s ratings. This model corrects for self-selection bias in comparing the differences in the determinants of the two ratings. The correlation coefﬁcients indicate that there is signiﬁcant self-selection in the realized Moody’s ratings while there is not in those of S&P. The parameter estimates suggest that the set of important determinants of the ratings of the two rating agencies is not identical. Minimum x2 tests of difference between the two agency ratings further suggest that there is a statistically signiﬁcant difference between the determinants of the ratings of the agencies. Split ratings appear to reﬂect differences in both the weight attached to speciﬁc determinants of the ratings and differences in the way the bonds are classiﬁed

SE, maximum log-likelihood

None

Both logit and discriminant models give very similar results. The earlier practice of changing the sample by removing a country after rescheduling was shown to have little effect on reducing the errors of the model. Three variables always seemed to be signiﬁcant in distinguishing countries which reschedule their debts from other countries, namely real GDP growth, a measure of debt (either total debt divided by exports or the current debt service ratio), and international reserves to imports ratio

S. Hoti and M. McAleer

Author(s)

Foreign reserves position, foreign capital inﬂows, dependence of a country of the international economy, domestic political variables

Logit Probit

ML ML

t-statistic, maximum log-likelihood

None

The effects of various factors that might inﬂuence the decision to reschedule are examined in this regionally focused study. Those factors whose effects are detected include external debt level and service payments, fraction of debts attracting variable interest rates, external reserves position, foreign capital inﬂows, external trade situation, terms of trade, position with IMF and domestic economic indicators

Oetzel et al. (2001)

None

Logit

ML

Correlation None coefﬁcients, mean, standard deviation, maximum log-likelihood, F-statistic, Cox and Snell R2 ; Nagelkerke R2 ; x2, p-value

Country risk indicators are adequate measures of country risk (proxied by currency ﬂuctuations) during periods of stability. However, these indicators and the corresponding lagged variables fail to predict periods of discontinuity in which the national currency depreciates by 10% or more or 40% or more in one month and appreciates by 10% or more in 1 month

Oral et al. (1992)

None

G-Logit, logit, classiﬁcation and regression trees (CART)

Judgement approach, multi-level approach to optimization, OLS(?), sequential binary splits

Coefﬁcients of correlation, Spearman correlation coefﬁcient, Kindallstan’s correlation coefﬁcient, mean value of absolute deviation

None

G-Logit is a better performer than logit and CART in explaining the country risk ratings of Institutional Investor with respect to performance criteria. With respect to the predictive capacity of the models, based on a ten-fold cross-validation, G-Logit is consistent in terms of identifying the signiﬁcant indicators, since it has the same set of variables for both 1982 and 1987. The results indicate that only some of the indicators considered at the beginning seem to be signiﬁcant in determining the country risk ratings

RahnamaMoghadam et al. (1991)

None

Probit

ML

t-statistic, R2 ; hit ratio, x2

None

Debt service ratio is insigniﬁcant in explaining the creditworthiness of a country. As expected, the probability of rescheduling is positively correlated with total debt service as a percentage of GNP, and with the percentage of debt held as

Country Risk Models: An Empirical Critique

Odedokun (1995)

(continued)

87

88

Appendix 2.2. Continued Author(s)

Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Descriptive Statistics

Diagnostics

Empirical Findings

RahnamaMoghadam (1995)

None

Probit

ML(?)

x2 ; Maddala R2 ; Cragg –Uhler R2 ; McFadden R2 ; Chow R2 ; t-statistic

None

East Asia and the Paciﬁc, South Asia, Europe and Mediterranean, and North Africa and the Middle East have not experienced signiﬁcant debt rescheduling. The results for Latin America and the Caribbean and for Africa, South of the Sahara show that the determinants of debt rescheduling vary signiﬁcantly between the two regions. The evidence suggest that inferences concerning debt rescheduling determinants of a speciﬁc region cannot reliably be drawn from studies of data pooled across all debtor LDC or from other regions. All economic and ﬁnancial variables, except the ratio of total international reserves over outstanding and disbursed debt, lack consistency in their predictive value for debt rescheduling. This implies that rescheduling determinants should be examined by region and possibly country-bycountry to determine what credit policies are appropriate. The results, however, support the hypothesis that the political environment is an important determinant of debt rescheduling

S. Hoti and M. McAleer

variable interest rate loans, but negatively correlated with international reserves to debt outstanding. One implication is that the use of variable interest loans should be limited in order to avoid the risk of political destabilization. The study suggests that the analysis of debt can best be accomplished by segmenting the world into groups of countries that exhibit similar geographical, structural and institutional characteristics

None

Linear single equations

OLS

t-statistic, correlation matrix for multi-collinearity, R2

None

Country risk rating, followed by the debt indicator and debt in default, is the most important determinant of the secondary market prices of developing countries debt. The implications of these results are that these factors should be of primary importance in making policies aimed at the reduction of the debt burden of developing countries

Rivoli and Brewer (1997)

None

Logit

ML

Maximum loglikelihood, SE, x2

None

The performance of the rescheduling models including and excluding political variables declined from the earlier period (1980 –1985) to the later one (1986 –1990). The inclusion of the political variables in the later period improves the performance of the models in predicting debt rescheduling. Armed conﬂicts variable was signiﬁcant in both periods. All economic variables except the amortization rate were signiﬁcant at the 1% level. These are the opposite of the effects the same variables have on perceptions of country creditworthiness in Brewer and Rivoli (1990)

Schmidt (1984)

None

Discriminant function, cluster analysis

Stepwise MDA method, Ward’s method, General minimization routine for non-linear functions, and hold out technique

Wilks’ l; error percentages

None

Logit analysis seems to do best when compared to the other three methods in identifying early warning of debt rescheduling. However, the results of logit analysis are acceptable only if relatively ‘fresh’ data are used. Thus, the method’s applicability depends upon the availability of the data. A solution to this problem could be the integration of qualitative information within the context of quantitative models. This proposition incorporates more subjective elements into the early warning of debt rescheduling and should result in bank-speciﬁc systems and not in a worldwide-accepted system

Country Risk Models: An Empirical Critique

Ramcharran (1999a)

(continued)

89

90

Appendix 2.2. Continued Author(s)

Recognition of Omitted Explanatory Variables

Model

Method of Estimation

Descriptive Statistics

Diagnostics

Empirical Findings

None

Log –linear single equations

OLS

Spearman rank correlation coefﬁcients

None

Secondary market analysis might be superior as it continuously reﬂects the changes in perceptions and expectations of bond traders and investors. The high rank correlations between the bond yield spreads vis-a`-vis US Treasury Bonds and the Institutional Investor country risk ratings seem to indicate that bond yield spreads may be a better reﬂection of country risk than loan spreads in the secondary market. The strong relation allows for the construction of a yield-spread ranking. From this ranking, it is possible to compare the country risk of different countries

Somerville and Tafﬂer (1995)

None

Linear discriminant model, Logit

Jack-knife approach, ML

Runs test statistic, None optimal cut-off value, Wilks’ l; x2 ; F-statistic, Mosteller –Wallace contribution, Hosmer –Lemeshow x2

Comparisons of the forecasting performance of the Institutional Investor (II) country risk ratings and multivariate models show that predictions of the II ratings are heavily biased towards the ‘arrears’ classiﬁcation, with no Type I error, but a very high Type II error rate. The predictions of the statistical models are more balanced with low levels of Type I and Type II errors. In terms of the overall performance, a larger role for formal models in credit allocation to developing countries is implied. If enough weight is placed on Type I errors, it is possible for the II ratings to achieve a lower misclassiﬁcation cost than the models. Using a cost-minimizing cut-off value, the II rating system outperforms the models by avoiding Type I errors (perceived by creditors as having the greater average cost), but it achieves this by giving a low rating to 39 developing countries that turn out to be creditworthy

S. Hoti and M. McAleer

Scholtens (1999)

Tafﬂer and Abassi (1984)

Banker judgement

Two-group linear discriminant function (‘early warning’ model)

Stepwise Fisher discriminant approach

Use Jack-knife approach and Lachenbruch hold-out procedure to correct for serial correlation

The ‘early warning’ discriminant model, developed to evaluate country risk and highlight rescheduling propensity, exhibits true ex ante predictive ability. 69% of the 73 rescheduling cases in 1979 –1983 period were correctly anticipated at a cost of low Type II errors. A large number of the latter were countries dependent on international aid to avoid rescheduling. In practice, the model indicated problems with certain oil producers and some countries experiencing short-term debt problems. The model can best be viewed as measuring a country’s underlying solvency more than its short-term liquidity position. Institutional Investor country risk rating was used to assess the predictive power of conventional bank approaches to assess country risk. High prediction error rates were observed in the results and the good credit ratings of many countries were unaffected even by the rescheduling event itself. An ‘early warning’ model is useful as an input into a loan ofﬁcer’s judgemental task

Country Risk Models: An Empirical Critique

Mosteller –Wallace contribution, conditionaldeletion F-statistic, coefﬁcient of variation of the Fisher standardized coefﬁcients, varimax rotated PCA for multicollinearity

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CHAPTER 3

Rating Risk Rating Systems Abstract This chapter provides a qualitative comparison of the country risk rating systems of 10 prominent risk rating agencies, namely Business Environment Risk Intelligence S.A., the Economist Intelligence Unit, Euromoney, Fitch IBCA, Institutional Investor, International Country Risk Guide, Moody’s, Political Risk Services, S.J. Rundt and Associates and Standard and Poor’s. The chapter also evaluates in detail the quantitative rating system of the International Country Risk Guide, as a representative of agency rating systems. Such an evaluation permits a critical assessment of the importance and relevance of agency rating systems and risk ratings. Keywords: rating agencies, risk ratings systems, component analysis, economic risk, ﬁnancial risk, political risk, country risk JEL classiﬁcations: C21, E44 3.1. Introduction The monograph focuses on agency country risk ratings, which represent the second most widely used dependent variable in the country risk literature (see Chapter 2, Table 2.8). Intended primarily as a measure of country creditworthiness, country risk ratings serve as an indicator of the probability of debt rescheduling, which is the most widely used dependent variable in the literature. The lower is a country’s creditworthiness, the higher is the associated risk in investing in the country, and the higher is the probability that the country will reschedule its future debt payments. As country risk ratings are provided by numerous risk rating agencies, and are used in making decisions regarding the creditworthiness of a country, it is essential that such ratings are analysed critically. The debt crises of the early 1980s, political changes that occurred in the former Communist Block countries in the late 1980s and early 1990s,

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the East Asian, East European and Latin American ﬁnancial and banking crises that have occurred since 1997, and ﬁnally the events of 11 September 2001 and their aftermath, show clearly that the risks associated with engaging in international operations have increased substantially. Such events have also become more difﬁcult to analyse and predict for decision makers in the economic, ﬁnancial and political sectors. The importance of country risk analysis is underscored by the existence of several prominent risk rating agencies. Rating agencies compile country risk ratings as measures of the ability and willingness of countries to service their ﬁnancial obligations. However, the accuracy of risk rating agencies with regard to any or all country risk measures is open to question. Country risk rating systems have been evaluated recently by Hoti (2005) and Hoti and McAleer (2004). This chapter is an extension of these surveys using information on an additional three risk rating agencies. A qualitative comparison is provided for the country risk rating systems of 10 prominent risk rating agencies. Seven agencies, namely the Economist Intelligence Unit, Euromoney, Institutional Investor, International Country Risk Guide, Moody’s, Political Risk Services, and Standard and Poor’s, have been selected as their risk ratings have frequently been used in the country risk literature (see Chapter 2). The remaining three agencies, namely Fitch IBCA, Business Environment Risk Intelligence S.A., and S.J. Rundt and Associates, have been selected given their major roles in international ﬁnancial market operations. A classiﬁcation of the 10 risk rating agencies is given according to the agency deﬁnition of country risk ratings, number of countries covered, frequency of the risk ratings, number and type of ratings compiled, number and type of risk component variables used, weights assigned to risk components, and the range associated with the risk ratings. The chapter also evaluates in detail the quantitative rating system of the International Country Risk Guide, as a representative of agency rating systems (see also Hoti and McAleer, 2004). Such an evaluation permits a critical assessment of the importance and relevance of agency rating systems and risk ratings. 3.2. Risk rating industry The increasing importance of country risk analysis by both ofﬁcial and private institutions is due to the fact that trade globalisation and open capital markets are risky elements that can cause ﬁnancial crises. Such rapid contagion effects can threaten the stability of the international ﬁnancial sector (Hayes, 1998). Furthermore, the increasing number of ﬁnancial crises in developing countries, and their associated costs to ofﬁcial

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institutions and private entities, are also major factors of risk that should be analysed. Given these developments, the need for a detailed assessment of country risk and its impact on international business operations is crucial. A primary function of country risk assessment is to anticipate the possibility of debt repudiation, default or delays in payment by sovereign borrowers (Burton and Inoue, 1985). Since the Third World debt crisis in the early 1980s, the number of country risk ratings compiled by commercial agencies such as Moody’s, Standard and Poor’s (S&P’s), Euromoney, Institutional Investor (II), Economist Intelligence Unit (EIU), International Country Risk Guide (ICRG), Political Risk Services (PRS), Fitch IBCA, Business Environment Risk Intelligence S.A. (BERI), and S.J. Rundt & Associates (R&A), has increased substantially. There are 130– 150 agencies around the world that provide risk ratings for a large number of countries. Moody’s, S&P’s and Fitch IBCA are widely regarded as the three major agencies in the risk rating industry (Setty and Dodd, 2003). Moody’s and S&P dominated the industry at the turn of the 21st century, with a combined market share of almost 80% of ratings sales revenue, while Fitch IBCA successfully captured the market as the third leading risk opinion provider on various issuers (Bhatia, 2002). Country risk ratings are measures of the ability and willingness of countries to service the ﬁnancial obligations to its foreign creditors and investors. Risk rating agencies employ different methods in determining country risk ratings and provide an independent analysis of country risk and a consistent method of risk assessment. These rating agencies combine a range of qualitative and quantitative information regarding alternative measures of economic, ﬁnancial and political risk into overall composite risk ratings. Generally, risk ratings are compiled for countries, bond issuers and/or speciﬁc issues of bonds. While country risk ratings reﬂect the creditworthiness of a country as a whole, issuer (bond) risk ratings reﬂect the ability of an entity (security) to meet its ﬁnancial obligations, such as interest, preferred dividends, or repayment of principal on a timely basis. Issuers, namely sovereign governments, banks, and corporate and public sector entities, seek agency risk ratings in order to establish relationships with international investors. In general, the risk rating assigned to the government of a country serves as a benchmark or ceiling for the ratings assigned to other borrowers in that country. Hence, a sovereign rating sets the parameters within which the private sector can operate (Fitch Ratings, 2002). This is of particular importance for developing countries, where publicly available information is limited. Country risk ratings help these countries to gain access to capital markets, and provide ofﬁcial institutions

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and private market operators with essential tools to assess and manage such risks. As discussed above, agency risk ratings play a central role in integrated capital markets. Consequently, the accuracy of risk rating agencies with regard to any or all country risk measures is crucial. Failure by the rating agencies to predict a number of major ﬁnancial crises demands a thorough evaluation of agency rating systems. Rating systems have changed, especially after the South East Asian, Russian and South American crises of 1997 –2002. These crises highlighted the need to accommodate factors such as contingent liabilities, adequacy of international reserves, relative likelihood of default on local currency against foreign currency sovereign debt, and assessment of individual debt instruments in selective default scenarios (Bhatia, 2002). Moreover, agency risk ratings may add to the instability of international ﬁnancial markets. Amato and Furﬁne (2003) argue that when risk rating agencies evaluate a risk rating, they overreact relative to the present state of the aggregate economy. Rating agencies tend to upgrade ratings at the peak of the business cycle, and downgrade near the trough, thereby exacerbating economic ﬂuctuations. This could be due to excessive optimism (pessimism) by rating agencies during the upturns (downturns). 3.3. Comparison of country risk rating methodologies Country risk refers broadly to the ability and willingness of a country or a borrowing entity residing in that country to repay its ﬁnancial obligations to its foreign creditors. While individual agencies use different deﬁnitions of country risk ratings, they all fall into this broad category. Importantly, a critical comparison among the 10 agency rating systems is presented. Unless otherwise stated, the information regarding the agency rating systems has been collated and extended substantially from the websites of the 10 risk rating agencies, the Default Risk: Rating Agencies (Default Risk, 2003), and the Foreign Investment Advisory Service Program (2003), which is a joint service of two leading multilateral development institutions, namely the International Finance Corporation and World Bank. Moody’s country risk rating is deﬁned as a measure of the ability and willingness of a country’s central bank to provide foreign currency to service the foreign debt held by the government and other borrowers in the country. This rating is not a direct evaluation of the creditworthiness of the government, but rather an assessment of the foreign liabilities of the country as a whole (Moody’s Investors Service, 2003a,c,d). Fitch IBCA deﬁnes its ratings as the ability of a sovereign country to generate

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the foreign exchange necessary to meet its obligations (Fitch Ratings, 2003a). As in the case of the Moody’s ratings, Fitch IBCA ratings refer to the outstanding foreign currency obligations incurred by both the government and private sector (Fitch Ratings, 2002). Unlike the Moody’s and Fitch IBCA ratings, S&P’s deﬁnes its country risk rating as a measure of a government’s ability and willingness to repay debt according to the terms of the debt. S&P’s ratings are sovereign ratings as they address the credit risk of the government and not of the other borrowers of a country (Howell, 2001). Institutional Investor (2003a), Euromoney (2003) and R&A (Rundt S.J. & Associates, Inc., 2003b) deﬁne their country risk ratings as measures of the creditworthiness of a country as a whole. These ratings measure the economic, ﬁnancial, political and overall performances of countries. The ICRG country risk rating is deﬁned as the ability and willingness of a country to ﬁnance its ofﬁcial, commercial, and trade debt obligations, and hence measures the economic, ﬁnancial and political structures of a country as a whole (The PRS Group, Inc., 2003). Similarly, BERI deﬁnes its country risk rating as a proﬁt opportunity recommendation, which assesses whether the business environment in a country merits investment, contracts for medium and long-term relationships, transaction-by-transaction trade, or no business relations. The rating measures the operating conditions and the political, foreign exchange and external accounts positions of a country (Business Environment Risk Intelligence S.A., 2003). The Economist Intelligence Unit (2003b) deﬁnes its country risk rating as a measure of the likelihood of a ﬁnancial crisis in a country that would affect foreign investors in that country. Moreover, the EIU ratings provide a measure of the general risk associated with investing in a country. Finally, PRS deﬁnes its rating as a measure of the likely changes in the level of political turmoil and government intervention that affect the business climate. These ratings are known as forecast ratings (Political Risk Services, 2003). Table 3.1 classiﬁes the 10 rating agencies according to the initial year of compilation of the country risk ratings. Clearly, Moody’s and S&P’s are the oldest agencies in the risk rating industry. Moody’s issued its ﬁrst country risk ratings just before World War I. S&P’s was formed after Poor’s Publishing and Standard Statistics merged in 1941. Like its predecessors, S&P’s continued to compile risk ratings for several sovereign bond issues (Bhatia, 2002). While EIU was founded in 1946, the debut year for EIU country risk ratings is not available (Economist Intelligence Unit, 2003a). S.J. Rundt & Associates was established in 1952 and has been compiling country risk ratings since that time (Rundt S.J. & Associates, Inc., 2003a). Thirteen years later, BERI was founded and published its ﬁrst country risk ratings.

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Table 3.1. Risk ratings debut year Agency

Year

Moody’s S&P’s EIU R&A BERI II PRS Euromoney ICRG Fitch IBCA

1914 1941 1946 1952 1966 1979 1979 1983 1984 2000

A number of agencies emerged around the onset of the Third World debt crisis. While both II and PRS risk ratings debuted in 1979, Euromoney published its ﬁrst ratings in 1983, followed by ICRG in 1984. Finally, Fitch IBCA was formed in 2000 after successive mergers in 1997– 1999 between Fitch Investor Services, International Banking Credit Analysis Ltd. (IBCA), and Duff and Phelps. Like its predecessors, Fitch IBCA continues to compile country risk ratings for several countries and debt issues (Fitch Ratings, 2003b). In Table 3.2, the 10 rating agencies are classiﬁed according to the number of countries rated, as of August 2003, except for Moody’s and S&P’s, for which the information is available to July 2002 (Bhatia, 2002). The number of rated countries ranges from 50 to 185. Of the 10 rating agencies, Euromoney’s coverage is the largest, compiling ratings for 185 countries, and BERI’s coverage is the smallest at 50. Table 3.2. Classiﬁcation by the number of countries covered Agency Euromoney ICRG II Moody’s EIU PRS R&A S&P’s Fitch IBCA BERI

Number of Countries 185 140 135 109 100 100 100 93 81 50

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The ICRG covers the second largest group of countries with 140, while II provides ratings for more than 135 countries (Howell, 2001). As of July 2002, Moody’s has been providing ratings for 109 countries, while S&P’s ratings cover the governments of 93 countries (Bhatia, 2002). Virtually every one of the countries covered by Moody’s participates in the world’s capital markets. Three rating agencies, namely EIU, PRS and R&A, provide ratings for 100 countries. The EIU covers key emerging and highly indebted countries that are monitored by its Country Risk Service (CRS), and R&A covers 100 countries monitored by its Financial Executive’s Country Risk Alert. Fitch IBCA ratings cover entities and governments in 81 countries, followed by BERI with ratings for 50 countries monitored by its Business Risk Service (BRS). Published country risk ratings are made available on a consistent basis by some agencies, while others provide ratings when such ratings are changed. Table 3.3 classiﬁes the 10 rating agencies according to the frequency of their ratings. Of the 10 rating agencies, ICRG is the only agency to provide consistent country risk ratings on a monthly basis. Business Environment Risk Intelligence S.A. publishes its ratings triannually in April, August and December. Similarly, R&A publishes its ratings tri-annually, with occasional country updates. The Economist Intelligence Unit publishes quarterly risk ratings with monthly updates on these ratings. Political Risk Services provides quarterly ratings with no updates, while II and Euromoney publish their ratings semi-annually in the March and September issues of these monthly magazines. Moody’s and S&P’s provide annual credit reports, Table 3.3. Classiﬁcation by frequency of ratings Agency ICRG EIU PRS BERI R&A II Euromoney Moody’s S&P’s Fitch IBCA a

With monthly ratings updates. With weekly ratings updates. c ‘NA’ denotes ‘not available’. b

Frequency of Ratings Monthly Quarterlya Quarterly Tri-annual Tri-annual Semi-annual Semi-annual Annuala Annualb NAc

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with monthly and weekly ratings updates, respectively. Finally, Fitch IBCA ratings are not published but, when a rating has been reviewed, the outcome is made available through the media or on the Internet (Fitch Ratings, 2002). Table 3.4 classiﬁes the 10 rating agencies according to the number of risk ratings compiled by each. The number of compiled ratings ranges from 1 to 10. Although Moody’s, S&P’s and Fitch IBCA compile ratings for both the issuer and speciﬁc debt instruments, the other seven agencies compile ratings only for the issuer. Of the 56 risk ratings, more than half are compiled by Moody’s, Euromoney and Fitch IBCA, with the remaining 27 ratings being compiled by S&P’s, ICRG, EIU, BERI, R&A, PRS, and II. For each nation, Moody’s publishes several types of ratings to capture divergent risks, including country ratings for both short- and long-term foreign currency securities (Moody’s Investors Service, 2003b). Moody’s ratings cover 10 major areas, namely long-term (bonds and preferred stock), issuer, bank deposits, bank ﬁnancial strength, national scale, managed fund, real estate fund, prime rating, and speculative grade liquidity. Country risk ratings act as sovereign ceilings or caps on ratings of foreign currency securities of any other borrowing entity, and account for foreign currency transfer risk and systemic country risk. Local currency guideline ratings, which indicate the highest rating level likely for debt issues denominated in the local currency, are also provided. Moreover, Moody’s system forecasts the likelihood that a nation will be able to avoid default under different stress scenarios, including a potential liquidity crisis (Howell, 2001). Like Moody’s, Euromoney offers 10 ratings for each country. These ratings include one composite country risk rating and nine component risk ratings, namely political risk, economic performance, debt indicators, debt Table 3.4. Classiﬁcation by number of ratings compiled Agency

Number of Ratings

Moody’s Euromoney Fitch IBCA S&P’s ICRG EIU BERI R&A PRS II

10 10 9 7 4 4 4 4 3 1

Total

56

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in default or rescheduled, credit ratings, access to bank ﬁnance, access to short-term ﬁnance, access to capital markets, and discount on forfeiting. The political risk rating is regarded as an industry speciﬁc assessment, and reﬂects the risk of non-payment or non-serving of a payment for goods and services, loans, trade-related ﬁnance and dividends, and the nonrepatriation of capital (Howell, 2001). Fitch IBCA offers both short- and long-term ratings to cover the spectrum of corporate, structured and public ﬁnance. These ratings are offered for 10 types of entities and issues, namely sovereigns, governments, structured ﬁnancings, corporations, debt, preferred/preference stock, bank loans, counterparties, and the ﬁnancial strength of insurance companies and ﬁnancial guarantors. Entity ratings assess the creditworthiness of the entity, while issue ratings take into account the relative preferential position of the holder of the security, and reﬂect the terms, conditions and covenants attached to that security. Fitch IBCA provides both local and foreign currency ratings. Unlike the local currency ratings, the foreign currency ratings account for foreign currency transfer risk. S&P’s ratings cover both local and foreign currency debt issued by governments (Standard and Poor’s, 2003b). Foreign currency ratings are distinguished from local currency ratings to identify those instances where sovereign risk makes them different for the same issuer. The ratings are provided for seven major areas, namely long-term debt, commercial paper, preferred stock, certiﬁcates of deposit, money market funds, mutual bond funds, and the claims-paying ability of insurance companies (Howell, 2001). Such ratings set the benchmark for ratings assigned to other issuers in the country. Moreover, S&P’s provides a rating outlook which assesses the potential direction of a long-term credit rating over the intermediate to longer term. In determining a rating outlook, consideration is given to any changes in economic and/or fundamental business conditions (Standard and Poor’s, 2003a). International Country Risk Guide, EIU, BERI and R&A each compiles four types of risk ratings. The ICRG ratings include one composite country risk rating, which reﬂects all risk aspects within a country, and three component risk ratings, namely economic, ﬁnancial and political. Moreover, the ICRG rating system provides rating forecasts, which reﬂect current, 1- and 5-year risk assessments. Projections of future conditions in different countries are categorised into ‘best’ and ‘worst’ case scenarios (International Country Risk Guide, 2003). Economist Intelligence Unit compiles one country risk rating and three speciﬁc investment ratings, namely currency risk (associated with accepting foreign exchange exposure against the US dollar), sovereign debt risk (associated with foreign currency loans to sovereign states), and

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banking sector risk (associated with foreign currency loans to banks). While the country risk rating is a composite indicator of country risk, the speciﬁc investment ratings reﬂect investment risks associated with ﬁnancial instruments. Similarly, BERI provides one composite country risk rating and three component risk ratings, namely political risk index, operations risk index and remittance and repatriation risk index. A country risk rating is a composite indicator of country risk and captures all components of risk in a country. Moreover, 1- and 5-year rating forecasts, reﬂecting assessments of future operating environments in countries, are also provided. S.J. Rundt and Associates compiles risk ratings for four types of risk, namely composite, socio-political, domestic economic and external accounts. The composite risk is an overall measure of country risk and reﬂects the three types of risk components. The R&A system offers a combination of risk ratings for each country, as well as a 1-year forecast. Political Risk Services provides a political risk model with three industry forecasts at the micro level, namely ﬁnancial transfers (banking and lending), foreign direct investment (such as retail, manufacturing, and mining), and exports to the host country market. Unlike the other nine rating agencies, PRS provides only industry-speciﬁc forecasts and not a composite country risk rating. Finally, II offers only one risk rating, namely a country risk rating. Institutional Investor compiles both six-month and 1-year records for its country risk rating. The rating indicates the economic, ﬁnancial and political changes in countries occurring in the previous sixth months and 1 year (Howell, 2001). Table 3.5. Number of risk component variables used Agency Fitch IBCA EIU R&A BERI Euromoney ICRG PRS Moody’s S&P’s II

ECO

FIN

POL

Others

Total

53 55 16 8 2 5 13 7 3 5

46 10 16 11 10 5 2 0 1 3

29 11 12 20 11 12 5 6 6 1

0 0 0 0 3 0 0 0 0 0

128 76 44 39 26 22 20 13 10 9

Notes: Economic, ﬁnancial and political risk ratings are denoted as ECO, FIN, and POL, respectively. The ‘Others’ category refers to a combination of agency ratings.

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Table 3.5 classiﬁes the 10 rating agencies according to the total number of risk component variables used. The total number of risk component variables used in the rating systems of the 10 agencies ranges from 9 (for II) to 128 (for Fitch IBCA). Of the remaining eight agencies, BERI, Euromoney, EIU, ICRG, PRS and R&A use at least 20 component variables to compile their ratings, while Moody’s and S&P’s use at least 10 component variables. In terms of the individual risk component variables, the number of economic risk variables used by each agency varies from 2 (for Euromoney) to 55 (for EIU). The EIU uses the largest number of economic risk variables, followed by Fitch IBCA (53 variables), R&A (16), PRS (13), BERI (8), Moody’s (7), ICRG and II (5 each), S&P’s (3), and Euromoney (2). In the case of ﬁnancial risk variables, the total number of variables used by each agency ranges from 0 (for Moody’s) to 46 (for Fitch IBCA). Of the remaining eight agencies, R&A uses 16 ﬁnancial variables, followed by BERI (11), EIU and Euromoney (10 each), ICRG (5), II (3), PRS (2), and S&P’s (1). The number of political risk variables used by each agency ranges from 1 (for II) to 29 (for Fitch IBCA). Of the remaining eight agencies, BERI uses 20 political variables, followed by ICRG and R&A (12 each), EIU and Euromoney (11 each), Moody’s and S&P’s (6 each), and PRS (5). Finally, regarding the ‘Others’ category, which refers to agency risk ratings, Euromoney is the only agency which considers risk ratings compiled by 3 other agencies, namely Moody’s, S&P’s, and Fitch IBCA. The classiﬁcation in Table 3.6 is given according to the type of risk component variables used in the rating systems of the 10 agencies. A total of 387 risk component variables are used in the agency rating systems. Almost half of the risk component variables used by these rating agencies are predominantly economic in nature, with the remainder being political or ﬁnancial. Political variables are the second most frequently used risk Table 3.6. Type of risk component variables used Variables

Frequency

Economic Political Financial Others

167 113 104 3

Total

387

Note: The ‘Others’ category refers to a combination of agency risk ratings.

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components. The ‘Others’ category refers to agency risk ratings, being used only by Euromoney. In terms of the rating system used to compile composite country risk ratings, BERI, EIU, Euromoney, ICRG and R&A differ from Moody’s, S&P’s, Fitch IBCA, PRS and II in that they calculate composite ratings using speciﬁc formulae, with predetermined weights assigned to each risk component. Table 3.7 reports the risk component weights used in the rating systems of BERI, EIU, Euromoney, ICRG and R&A. The economic risk variables have the highest weight of 55% for EIU, followed by BERI and R&A (each with a weight of 33.3%), and Euromoney and ICRG (25% each). For ﬁnancial risk component variables, Euromoney assigns the highest weight at 40%, followed by BERI and R&A (each 33.3%), ICRG (25%), and EIU (23%). For the political risk component variables, ICRG assigns the highest weight at 50%, followed by BERI and R&A (each 33.3%), Euromoney (25%), and EIU (22%). Finally, in order to obtain the overall country risk score, Euromoney also assigns a weight of 10% to the agency ratings component. With respect to the rating systems of Moody’s, S&P’s, Fitch IBCA, PRS and II, composite risk ratings are determined on a subjective basis. For each country, Moody’s analysts weight the risk component variables according to their assessment of the likelihood of default by a country and its borrowers. Similarly, in determining a risk rating, S&P’s analysts weight the risk component variables based on their assessments of credit fundamentals affecting each government, and perceptions of global systemic factors that inﬂuence the timing and magnitude of sovereign defaults (Howell, 2001). Fitch IBCA rating system draws on instances of default and near default to establish key indicators of distress. The risk model incorporates these indicators and gives a percentage score to sovereign borrowers, which is used to derive the long-term ratings. Political Risk Services system compiles forecast ratings based on the component risk Table 3.7. Weights assigned to risk component variables (in %) Agency

ECO

FIN

POL

Others

Total

BERI EIU Euromoney ICRG R&A

33.3 55 25 25 33.3

33.3 23 40 25 33.3

33.3 22 25 50 33.3

0 0 10 0 0

100 100 100 100 100

Notes: Economic, ﬁnancial and political risk ratings are denoted as ECO, FIN, and POL, respectively. The ‘Others’ category refers to a combination of agency ratings.

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variables, and weights them according to the assessed potential economic, ﬁnancial and political risks to business investments and trade. Institutional Investor differs from the other nine agencies in that it uses no internal rating system. For each country, II asks 75 – 100 leading international banks to rate the risk components. The individual ratings are weighted using II’s formula, with greater weights assigned to responses based on the extent of a bank’s worldwide exposure and the degree of sophistication of a bank’s country risk model. The names of the participating banks are kept strictly conﬁdential. In the country risk literature, the II country risk assessment is known as the banker’s judgment (Institutional Investor, 2003b). Finally, in Table 3.8 the 10 rating agencies are classiﬁed according to the type and range of gradings assigned to country risk ratings. Institutional Investor, Euromoney, and ICRG provide quantitative country risk ratings, which range from 0 (lowest) to 100 (highest), while R&A ratings are rated on a scale of 1 (best) to 10 (worst). On the other hand, Moody’s, S&P’s, EIU, and PRS publish qualitative letter ratings. The country risk ratings for Moody’s range from Aaa (highest) to C (lowest), with a total of 23 letter categories; for EIU from A (highest) to E (lowest), with ﬁve letter categories; and for PRS from A þ (highest) to D 2 (lowest), with 12 letter categories. For both S&P’s and Fitch IBCA, the ratings are graded on a scale of AAA (highest) to D (lowest). However, S&P’s ratings have 23 letter categories, while Fitch IBCA ratings have 24. Moreover, the letter ratings for EIU and PRS are derived from numerical scores assigned to each risk rating considered. Apart from the R&A ratings, the lower (higher) is a given risk rating, the higher (lower) is the associated risk.

Table 3.8. Types of risk rating grades Agency R&A BERI II Euromoney ICRG Moody’s S&P’s Fitch IBCA EIU PRS

Grading Range 1 to 10 0 to 100 0 to 100 1 to 100 0 to 100 Aaa to C AAA to D AAA to D A to E A þ to D 2

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3.4. ICRG country risk ratings As discussed in the previous section, ICRG is the only risk rating agency to provide consistent monthly data over an extended period. Since January 1984, the International Country Risk Guide (ICRG) has been compiling economic, ﬁnancial, political and composite risk ratings for 90 countries on a monthly basis. As of October 2004, the four risk ratings were available for a total of 140 countries and 144 entries, the extra four entries relating to the former sovereign states of Czechoslovakia, East Germany, West Germany and the USSR. According to the ICRG, its risk ratings have been referred to by analysts at the IMF, World Bank, United Nations, and other international institutions as a standard against which agency ratings can be measured. The ICRG has been acclaimed by publications such as Barron’s and The Wall Street Journal for the strength of its analysis and rating system (http://www.icrgonline.com). Several issues relating to the ICRG coverage of the listed countries should be emphasised. Some sovereign states, such as the former Soviet republics and the former Communist Block countries, have been covered only recently. Furthermore, structural changes are, in general, not accommodated in the risk ratings. The ICRG rating system was adjusted in late-1997 to reﬂect the changing international climate created by the ending of the Cold War. Prior to this structural change, the ﬁnancial risk ratings were highly subjective because of the lack of reliable statistics. By 1997, the risk assessments were made by the ICRG on the basis of independently generated data, such as from the IMF, which could be referenced consistently over time. Until the dissolution of the former Federal Republic of Yugoslavia, ICRG covered Yugoslavia which comprised all six republics. After the dissolution, the country referred to the Federal Republic of Yugoslavia, comprising the Republics of Montenegro and Serbia, the latter including the UN-administered southern province of Kosovo and the northern province of Vojvodina. However, Yugoslavia ceased to be in February 2003, when the parliaments of Serbia and Montenegro approved a constitutional charter for the union of Serbia and Montenegro. As of August 2003, the entry for Yugoslavia in the ICRG series was changed to Serbia and Montenegro. Since December 1998, ICRG has been covering separately two of the former Yugoslavian republics, namely Croatia and Slovenia, which are now internationally recognized sovereign states. Data for the other two new sovereign states, namely Bosnia-Herzegovina and the Former Yugoslav Republic of Macedonia, are not currently available. The ICRG coverage of the former East and West Germany also merits discussion. After the fall of the Berlin Wall in November 1989,

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East and West Germany were reunited, so there is only one entry for Germany in the ICRG series from October 1990. Data for the former West Germany and East Germany are available separately for January 1984– September 1990 and June 1984– September 1990, respectively. The ICRG rating system comprises 22 variables representing three major components of country risk, namely economic, ﬁnancial and political. These variables essentially represent risk-free measures. There are ﬁve variables representing each of the economic and ﬁnancial risk components, while the political component is based on 12 variables. Economic risk rating measures a country’s current economic conditions. In general, when a country’s strengths outweigh its weaknesses, it presents a low economic risk, and when its weaknesses outweigh its strengths the country presents a high economic risk. This permits an assessment of the ability to ﬁnance its ofﬁcial, commercial, and trade debt obligations. The ﬁve economic variables, and the range of risk points assigned to each, are as follows: † † † † †

GDP per Head of Population (0– 5); Real Annual GDP Growth (0– 10); Annual Inﬂation Rate (0– 10); Budget Balance as a Percentage of GDP (0– 10); Current Account Balance as a Percentage of GDP (0– 15).

Financial risk rating is another measure of a country’s ability to service its ﬁnancial obligations. This rating assesses a country’s ﬁnancial environment based on the following ﬁve ﬁnancial variables and their associated risk points: † Foreign Debt as a Percentage of GDP (0– 10); † Foreign Debt Service as a Percentage of Export in Goods and Services (0– 10); † Current Account as a Percentage of Export in Goods and Services (0– 15); † Net Liquidity as Months of Import Cover (0 –5); † Exchange Rate Stability (0– 10). Political risk rating measures the political stability of a country based on socio-political factors. This risk affects the country’s ability and willingness to service its ﬁnancial obligations. The 12 political risk variables, and the range of risk points assigned to each, are as follows: † Government Stability (0 – 12); † Socio-economic Conditions (0– 12);

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Investment Proﬁle (0– 12); Internal Conﬂict (0– 12); External Conﬂict (0– 12); Corruption (0– 6); Military in Politics (0– 6); Religious Tensions (0 – 6); Law and Order (0 – 6); Ethnic Tensions (0– 6); Democratic Accountability (0– 6); Bureaucracy Quality (0– 4).

Using each set of variables, a separate risk rating is created for the three components. The ﬁve variables for the economic risk rating are weighted equally to give a score of 50 points, the ﬁve variables for the ﬁnancial risk rating are weighted equally to give a score of 50 points, and the 12 variables for the political risk rating are weighted equally to give a score of 100 points. As the composite risk rating is obtained by dividing the sum of the three component risk ratings by 2, the economic and ﬁnancial components account for 25% each, and the political component accounts for 50% of the composite risk rating. In all cases, the lower (higher) is a given risk rating, the higher (lower) is the associated risk. In essence, the country risk rating is a measure of country creditworthiness. The ranges of the ICRG risk ratings for economic, ﬁnancial, political and composite risk are 0 – 50, 0 – 50, 0 – 100, and 0 – 100, respectively. Hence, the assigned risk score reﬂects the degree of risk associated with a risk rating. As reported in Table 3.9, the ICRG range of 0 – 49.9% reﬂects a very low rating and a very high associated risk. High risk holds for the risk range of 50 –59.9%, moderate risk for 60 – 69.9%, low risk for 70– 79.9%, and very low risk for 80– 100%. In order to facilitate direct comparisons, in this monograph the range of the four risk ratings is given as 0– 100%.

Table 3.9. Degree of risk Description of Risk Very high High Moderate Low Very low

Range (in %) 0 – 49.9 50.0 – 59.9 60.0 – 69.9 70.0 – 79.9 80 – 100

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3.5. Conclusion This chapter provided a qualitative comparison of the risk rating systems of 10 leading commercial agencies of country risk, namely Business Environment Risk Intelligence S.A., Economist Intelligence Unit, Euromoney, Fitch IBCA, Institutional Investor, International Country Risk Guide, Moody’s, Political Risk Services, S.J. Rundt and Associates, and Standard and Poor’s. Classiﬁcations of the 10 risk rating agencies was given according to the agency deﬁnition of country risk ratings, number of countries covered, frequency of the risk ratings, number and type of ratings compiled, number and type of risk component variables used, weights assigned to risk components, and the given range for the risk ratings. Such an evaluation permitted a critical assessment of the importance and relevance of agency rating systems, and a critical comparison of the 10 agency rating systems. Moreover, the chapter analysed in detail the rating system of the International Country Risk Guide (ICRG), as a representative of the agency rating systems. The ICRG is the only risk rating agency to provide consistent monthly risk ratings. In order to assess the importance and relevance of agency risk ratings, the ICRG risk ratings will be critically evaluated and assessed empirically in the following chapters. References Amato, J.D. and C.H. Furﬁne (2003), “Are credit ratings procyclical?”, Bank for International Settlements Working Paper 129. Bhatia, A.V. (2002), “Sovereign credit ratings methodology: an evaluation”, International Monetary Fund Working Paper 02/170. Burton, F.N. and H. Inoue (1985), “An appraisal of the early-warning indicators of sovereign loan default in country risk evaluation systems”, Management International Review, Vol. 25, pp. 45 – 56. Business Environment Risk Intelligence S.A. (2003), Business risk services, http://www. beri.com/brs.htm. Default Risk (2003), Rating agencies, http://www.defaultrisk.com/rating_agencies.htm. Economist Intelligence Unit (2003a), About the economist intelligence unit, http://www. eiu.com/. Economist Intelligence Unit (2003b), Country risk service, http://store.eiu.com. Euromoney (2003), Polls, awards and rankings, http://www.euromoney.com. Fitch Ratings (2002), Fitch Sovereign Ratings: Rating Methodology, Fitch Inc. and Fitch Ratings Ltd., New York. Fitch Ratings (2003a), Fitch ratings deﬁnitions, http://www.ﬁtchibca.com/corporate/ ratings/deﬁnitions/index.cfm. Fitch Ratings (2003b), Sovereigns and subnationals, http://www.ﬁtchibca.com/corporate/ sectors/sector.cfm?sector_ﬂag ¼ 5&marketsector ¼ 1.

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Foreign Investment Advisory Service Program (2003), Investment climate indicators, http://www.ﬁas.net/investment_climate.html. Hayes, N.L. (1998), “Country risk revisited”, Journal of Lending and Credit Risk Management, Vol. 80, p. 61. Hoti, S. (2005), “Comparative analysis of risk ratings for the East Europe region”, Mathematics and Computers in Simulation, in press. Hoti, S. and M. McAleer (2004), “An empirical assessment of country risk ratings and associated models”, Journal of Economic Surveys, Vol. 18(4), pp. 539– 588. Howell, L.D. (2001), The Handbook of Country and Political Risk Analysis, 3rd edition, New York: The PRS Group. Institutional Investor (2003a), Research and rankings, http://www.institutionalinvestor. com/premium/rr/index.asp. Institutional Investor (2003b), About II, http://www.institutionalinvestor.com/IIOnline/ aboutus/default.asp. International Country Risk Guide (2003), About ICRG, http://www.icrgonline.com. Moody’s Investors Service (2003a), Introduction to Moody’s, http://www.moodys.com. Moody’s Investors Service (2003b), Rating approach, http://www.moodys.com. Moody’s Investors Service (2003c), Rating deﬁnitions, http://www.moodys.com/moodys/ cust/ratingdeﬁnitions/rdef.asp. Moody’s Investors Service (2003d), Understanding risk, http://www.moodys.com. Political Risk Services (2003), PRS Methodology: The Coplin-O’Leary Rating System, http://www.prsonline.com/methods.asp. Rundt, S.J. & Associates, Inc. (2003a), About us, http://www.rundtsintelligence.com/index. asp. Rundt, S.J., & Associates, Inc. (2003b), The ﬁnancial executive country risk alert, http:// www.rundtsintelligence.com/index.asp. Setty, G. and R. Dodd (2003), “Credit rating agencies: Their impact on capital ﬂows to developing countries”, Financial Policy Forum Special Policy Report, p. 6. Standard and Poor’s (2003a), Credit ratings: ratings criteria, http://www.standardandpoors. com. Standard and Poor’s (2003b), Company history, http://www.standardandpoors.com. The PRS Group, Inc. (2003), Our methods, http://www.prsgroup.com/commonhtml/ methods.html.

CHAPTER 4

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries Abstract Monthly International Country Risk Guide country risk ratings and risk returns are evaluated for 120 countries representing eight geographic regions. This chapter provides, for the ﬁrst time, a comparative assessment of the trends and volatility of country risk ratings for these 120 countries, and highlights the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating. Keywords: ICRG, economic risk, ﬁnancial risk, political risk, risk ratings, risk returns, volatilities, component analysis, international comparison JEL classiﬁcations: E44, F34, O11, O19, O57 4.1. Introduction In order to assess the relevance and accuracy of the risk ratings compiled by various agencies, it is essential that such ratings are analysed critically. As discussed in Chapter 3, agency risk ratings play a major role in international ﬁnancial market operations. For this reason, the monograph focuses on monthly economic, ﬁnancial, political and composite risk ratings compiled by the International Country Risk Guide (ICRG, 2002) as representatives of agency risk ratings. One-hundred and twenty countries, for which ICRG ratings are available, have been selected to represent eight geographic regions. This chapter provides a detailed evaluation of ICRG risk ratings and risk returns, where the latter is deﬁned as the monthly percentage change in the respective risk ratings, namely: Returnst ¼

Ratingst 2 Ratingst21 Ratingst21

£ 100

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where ‘Returnst’ are the country risk returns for month t; and ‘Ratingst’ are the country risk ratings for month t: For each of the 120 selected countries, the trends and associated volatility of the four country risk ratings and risk returns are analysed according to economic, ﬁnancial and political environments in the country. This chapter provides, for the ﬁrst time, a comparative assessment of the trends and volatility of country risk ratings of 120 countries covered by the ICRG, and highlights the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating. 4.2. One-hundred and twenty selected countries Following the ICRG classiﬁcation, the 120 countries to be examined are grouped according to the monthly starting date of ICRG coverage and geographic region (see Appendix 4.1). For the empirical discussion and analysis presented here, the ﬁnal month of ICRG coverage is May 2002. The 120 countries are selected to represent eight geographic regions, namely Central and South Asia (Bangladesh, India, Pakistan, Sri Lanka), East Asia and the Paciﬁc (Australia, Brunei, China, Hong Kong, Indonesia, Japan, Malaysia, Mongolia, New Zealand, North Korea, Papua New Guinea, Philippines, Singapore, South Korea, Taiwan, Thailand, Vietnam), East Europe (Albania, Bulgaria, Czech Republic, Hungary, Poland, Romania, Russia, Slovak Republic, Yugoslavia), Middle East and North Africa (Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates, Yemen), North and Central America (Bahamas, Canada, Costa Rica, Cuba, Dominican Republic, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Trinidad and Tobago, USA), South America (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, Venezuela), Sub-Saharan Africa (Angola, Botswana, Burkina Faso, Cameroon, Congo, Coˆte d’Ivoire, Democratic Republic of Congo, Ethiopia, Gabon, Ghana, Guinea, Kenya, Liberia, Malawi, Mali, Mozambique, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Uganda, Zambia, Zimbabwe) and West Europe (Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom). The 120 countries represent eight geographical regions, as reported in Table 4.1. With 26 countries, Sub-Saharan Africa has the largest representation, followed by West Europe, with 21 countries, Middle East and North Africa with 18 countries, East Asia and the Paciﬁc with 17

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Table 4.1. Number of countries by geographic region Geographic Region Central and South Asia East Asia and the Paciﬁc East Europe Middle-East and North Africa North and Central America South America Sub-Saharan Africa West Europe Total

Number of Countries 4 17 9 18 15 10 26 21 120

countries, and North and Central America with 15 countries. South America with 10 countries is followed closely by East Europe with nine countries. The Central and South Asia has the smallest representation, with only four countries. The ICRG has been compiling ratings for the four countries in the Central and South Asia region since January 1984. Of the 17 countries in the East Asia and the Paciﬁc, only 10 countries have been covered since January 1984, with the ratings for Brunei, China, Mongolia, Papua New Guinea and South Korea available from November 1985, December 1984, April 1986, May 1984 and March 1985, respectively. The ratings for North Korea and Vietnam are available from October 1985. Of the nine East Europe countries, the ratings for Yugoslavia are available from January 1984, for Hungary and Romania from August 1984, for Bulgaria and Poland from December 1984, for Czech Republic and Slovak Republic from January 1993, for Albania from October 1985 and for Russia from April 1992. For the Middle East and North Africa region, 14 of the 18 countries have been covered from January 1984. The ratings for Bahrain and Yemen are available from September 1984 and for Oman and Qatar from July 1984 and August 1984, respectively. Ratings for 13 of the 15 North and Central America countries are available from January 1984, with the ratings for the Bahamas and Cuba available from December 1984 and October 1985, respectively. All 10 countries representing the South America region have been covered from January 1984. Only 16 of the 26 Sub-Saharan Africa countries have been covered from January 1984. Of the remaining 10, the ratings for Angola, Guinea and Mozambique are available from October 1985, for Botswana from October 1984, for Burkina Faso from May 1985, for Congo from April 1985, for Coˆte d’Ivoire from September 1986, for Ethiopia from November 1984 and for Sierra Leone from January 1985.

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Ratings for 17 of the 21 West Europe countries are available from January 1984, for Cyprus and Luxembourg from December 1984, for Malta from April 1986 and for Germany from January 1985. Overall, as reported in Table 4.2, 85 countries have been covered by ICRG since January 1984, one since May 1984, one since July 1984, four since August 1984, two since September 1984, one since October 1984, one since November 1984, ﬁve since December 1984, two since January 1985, one since March 1985, one since April 1985, one since May 1985, seven since October 1985, one since November 1985, one since December 1985, two since April 1986, one since September 1986, one since April 1992 and two since January 1993. 4.3. Risk ratings, risk returns and volatilities Risk ratings, risk returns and the associated volatilities for the 120 selected countries are given in Figures 4.1 – 4.120. Risk returns are deﬁned as the monthly percentage change in the respective risk ratings. For each country, the risk ratings (alternatively, risk returns) are denoted ECO-R, FIN-R, Table 4.2. Number of countries by ICRG starting date ICRG Starting Date January 1984 May 1984 July 1984 August 1984 September 1984 October 1984 November 1984 December 1984 January 1985 March 1985 April 1985 May 1985 October 1985 November 1985 December 1985 April 1986 September 1986 April 1992 January 1993 Total

Number of Countries 85 1 1 4 2 1 1 5 2 1 1 1 7 1 1 2 1 1 2 120

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POL-R and COM-R for the economic, ﬁnancial, political and composite risk ratings, respectively. Volatility is deﬁned as the squared deviation of each observation from the respective sample mean risk ratings or risk returns, namely: VolatilityðRatingsÞt ¼ ðRatingst 2 RatingsÞ2 and VolatilityðReturnsÞt ¼ ðReturnst 2 ReturnsÞ2 where Ratings and Returns are the sample mean values of Ratingst and Returnst ; respectively. The volatilities for the economic, ﬁnancial, political and composite risk ratings (alternatively, risk returns) are denoted ECO-V, FIN-V, POL-V and COM-V, respectively. There are substantial changes in the trends of the risk ratings, as well as in their associated volatilities. Similarly, substantial differences are evident in the risk returns, as well as in their volatilities. Information on the economic, ﬁnancial and political proﬁles and backgrounds for the 120 countries has been collated and extended substantially from ﬁve sources, namely the US Department of State (2003, 2004): Countries and Regions, BBC News (2003, 2004): Country Proﬁles and Timeline, The Economist (2003): Country Brieﬁngs, UK Trade and Investment (2004): Country Information, and The World Factbook for 2002 and 2004, prepared by the Central Intelligence Agency (2003, 2004). 4.3.1. Central and South Asia Figures 4.1 – 4.4 present the four risk ratings, risk returns and the associated volatilities for the four Central and South Asia countries, namely Bangladesh, India, Pakistan and Sri Lanka. The four risk ratings, risk returns and volatility in Figure 4.1 are for Bangladesh, a poor and overpopulated country. With limited coal and oil reserves and a weak industrial base, primary assets include vast human resources, rich agricultural land, abundant water and substantial natural gas reserves. Although 50% of GDP is generated through the services sector, the major employer is agriculture, and predominantly rice production. However, agriculture cannot absorb the rapidly growing labour force. Major impediments to growth include frequent natural disasters, inefﬁcient state-owned enterprises, poor infrastructure, slow energy resource exploitation, insufﬁcient power supplies and slow economic reforms. Political power struggles and corruption have hampered progress. Major efforts have been made to meet the food needs of its increasing population through increased domestic production and imports. The resulting large

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trade deﬁcit is ﬁnanced largely by aid receipts and remittances from workers overseas. Bangladesh was established when East Pakistan and West Pakistan separated in 1971. Since independence, Bangladesh has received large aid and loan funds from foreign donors. After falling to early 1988, economic risk rating rose substantially to 1995, but fell to 54 by late 1998 due to internal factors and the Asian crisis. The rating rose in 1999 and remained ﬂat in the high 1970s until 2002, with mild volatility and peaks from 1989 to 1991 and 1997 to 1999. On the other hand, ﬁnancial risk rating fell to 1989, rose by 50– 70 in 1999 and fell slightly to 2002, with higher volatility prior to 1993. Reﬂecting the political climate, political risk rating fell from 40 to 28 by 1989, rose to 64 by 1997 and fell to 52 by 2002, with a clustering of volatility. Northeast India, Bangladesh and Nepal are extremely poor. With Maoist rebels gaining strength in Nepal, uprisings in Northeastern India, and the Communist Party in West Bengal retaining power, a crisis in Bangladesh would add greatly to regional instability. However, Bangladesh is one of the most democratic states within the Muslim world and a leading voice among the least developed countries. Overall, the composite risk rating reﬂects the trends and volatility in all three component risk ratings. Figure 4.2 plots the risk ratings, risk returns and volatility for India, the world’s second most populous country. Almost 70% of the population relies on agriculture, while 25% lives below the poverty line. However, the large and increasing middle class is becoming wealthy. India has a mixed economy, based on services, industry and agriculture. Government controls on foreign trade and investment have decreased, and privatization is progressing. The economy has grown at 6% since 1990, thereby reducing poverty by about 10%. Given the large English-speaking and well-educated population, India aspires to become a major exporter of software services and software workers. Despite strong growth, the continuing public-sector budget deﬁcit, at 10% of GDP, is of serious concern. Growth is hampered by poor infrastructure, bureaucracy, corruption, a rigid labour market and foreign investment controls. India is continuing with market-oriented reforms that began in 1991. Recent reforms include liberalized foreign investment, exchange rates and the industrial sector, reductions in trade barriers, ﬁnancial and ﬁscal reforms, and intellectual property rights protection. Overall, the economic risk rating rose from 53 in 1984 to 69 in 2002, but did not exceed 75. Falling trends were observed from 1986 to 1992 and 1995 to 1999, with a noticeable clustering of volatility. Financial risk rating fell to 1991, after which it rose by almost 40 and reached 82 by 2002. Except for 1990 – 1992, ﬁnancial risk rating was less volatile than the economic risk rating. India became a British colony in the 19th century, with non-violent resistance

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to Britain under Gandhi and Nehru leading to independence in 1947. The subcontinent was divided into the secular state of India and the smaller Muslim state of Pakistan. A 1971 war between the two countries led to the creation of Bangladesh. After reaching a low of 30 in 1991, the political risk rating rose to 69 by 1997 and fell to 55 by 2002, with a clustering of volatility. Serious concerns include the dispute with Pakistan over Kashmir, massive overpopulation, environmental degradation, extensive poverty, and ethnic and religious friction. Overall, the composite risk rating closely reﬂects the trend and volatility in the political risk rating. Figure 4.3 presents the four risk ratings, risk returns and volatility for Pakistan, a poor and underdeveloped country. In 1988, IMF-supported structural reforms were launched in response to persisting ﬁscal and external deﬁcits. Some barriers to foreign trade and investment were removed, the ﬁnancial system reformed, foreign exchange controls reduced and state-owned enterprises privatized. The average annual growth rate was 6% during the 1980s and early 1990s. However, growth slowed by 2002, due to external and internal shocks, such as devastating ﬂoods and political uncertainty in 1992– 1993, and the Asian crisis in 1997– 1998. Structural reforms were slow in reducing the budget and current account deﬁcits, increasing infrastructure projects and curbing the rupee depreciation, and were hindered by economic sanctions imposed by the G7 after Pakistan’s nuclear tests in 1998. Following the 1999 military coup, new economic stabilization reforms were launched, and economic risk rating fell to 1999 and rose to 2002. The rating was very volatile only in 1999, with little volatility elsewhere. While long-term prospects remain uncertain, medium-term prospects for job creation and poverty reduction have improved. After a period of no trend, ﬁnancial risk rating fell to 34 in 1991, rose to 72 by 1996, fell to 51 in 1998 and rose to 2002, with a clustering of volatility. Foreign reserves have increased due to strong export growth and steady worker remittance, but foreign direct investment remains low due to security and political uncertainty. The 1947 separation of British India into Pakistan and India was followed by a third war in 1971, which resulted in East Pakistan separating to become Bangladesh. Corruption, inefﬁciency and military rule have led to political instability, with military rule re-imposed in 1999 after the ousting of a civilian government, amid economic and security problems. The coup leader, General Musharraf, became President in 2001. Tension persists with India over Kashmir amid international fears of a regional arms race. Political risk rating fell to 27 in 1990, rose to 65 in 1997 and fell to 2002, with a clustering of volatility. Overall, the composite risk rating closely reﬂects the trends and volatility in the ﬁnancial and political ratings.

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Figure 4.4 presents the four risk ratings, risk returns and volatility for Sri Lanka. After abandoning central economic planning in 1977, the government deregulated, privatized and opened the economy to international competition. The ethnic disputes of 1983 and the People’s Liberation Front (JVP) uprising in the late 1980s hampered economic reforms, harmed tourism and caused political uncertainty. After the defeat of the JVP, privatization and export-oriented growth helped revive the economy, and GDP grew at an average of 5.5% in the early 1990s. A drought and deteriorating security situation lowered growth to 3.8% in 1996. The economy recovered in 1997 –2000 with average growth of 5.3%, but Sri Lanka had the ﬁrst contraction since independence in 2001. Negative growth of 1.4% in 2001 was due to power shortages, severe budgetary problems, global slowdown and persisting civil conﬂicts. Foreign exchange reserves fell from 1999 to 2000, and the rupee was ﬂoated in early 2001, leading to a signiﬁcant depreciation. Growth rose to 4.0% in 2002 due to lower interest rates, stronger domestic demand, higher tourist arrivals, a revival of the stock exchange, a stable rupee and higher foreign direct investment. Economic risk rating rose to 75 by 1995 and fell to 2002, with two major falls in 1996 and 1997, and a small rise in 2001– 2002. There is little volatility, apart from 1985 to 1988, with peaks in 1996 and 1997. Financial, political and composite risk ratings have similar patterns. Overall, the three ratings fell to 1990, rose to 1995, had falling trends to 2001 and rose considerably to 2002. In general, composite risk rating reﬂects the volatility in ﬁnancial and political risk ratings. The island was occupied by the Portuguese in the 16th century and the Dutch in the 17th century, was ceded to Britain in 1796, became a crown colony in 1802, was united under British rule in 1815, became independent in 1948 as Ceylon, and changed to Sri Lanka in 1972. Tensions between the Sinhalese majority and Tamil separatists erupted in violence in the mid1980s, resulting in many thousands of deaths. After two decades of ﬁghting, the government and Liberation Tigers of Tamil agreed to a ceaseﬁre in 2001 that was mediated by Norway. 4.3.2. East Asia and the Paciﬁc Figures 4.5 – 4.21 present the four risk ratings, risk returns and the associated volatilities for the 17 East Asia and the Paciﬁc countries, namely Australia, Brunei, China, Hong Kong, Indonesia, Japan, Malaysia, Mongolia, New Zealand, North Korea, Papua New Guinea, Philippines, Singapore, South Korea, Taiwan, Thailand and Vietnam. The four risk ratings for Australia are presented in Figure 4.5. Australia is rich in natural resources, with a small domestic and developed market

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economy, dominant services sector, and agricultural and mining sectors signiﬁcant for exports. Structural reforms in the 1980s transformed an inward-looking and import-substituting economy to an internationally competitive economy with an export orientation. Owing to these reforms, Australia was one of the fastest growing OECD economies throughout the 1990s. Ultimately, the aim is to become a competitive exporter of valueadded manufactured products, services and technologies. The economic risk rating followed a generally increasing trend, with a clustering of volatility until 1998. After a period of fast growth, the risk rating followed a downward trend from 1998 to 1999 due to falling investments and rising debt. However, in 1999 the rating started to increase as the economy grew faster than both the US and EU economies. The rating fell again in late 2000, following a downturn caused by the implementation of the GST. After 2001 the Australian economy strengthened and the rating rose. There is a noticeable structural change in the ﬁnancial risk rating in 1997 when the rating decreased by almost 20 points, prior to which there was some variation but no trend. Consequently, while there is substantial volatility in the rating after 1997, there is little volatility prior to 1997. With the introduction of the GST, the ﬁnancial rating fell, after which it followed an increasing trend but remained relatively low. The political risk rating decreased until 1991, when Australia sent troops to assist US forces in the Gulf conﬂict, and then increased, with an associated clustering of volatilities. When John Howard became Prime Minister at the 1996 elections, this led to an increased risk rating until late 1997. The rating fell, but started to increase after Howard’s re-election in October 1998. The rating fell again in 1999 when Australia led an international coalition force to restore order in East Timor. Not surprisingly, the terrorist attacks of September 11, 2001 had a negative impact on the rating for Australia. As a weighted sum of the three component risk ratings, the composite risk rating for Australia had an increasing trend in the middle of the sample, after which the rating decreased and then increased. There is comparable volatility in the composite risk rating relative to the economic and political risk ratings. Figure 4.6 presents the four risk ratings, risk returns and volatilities for Brunei. With a small population and extensive oil and natural gas deposits, Brunei enjoys one of the world’s highest standards of living. While oil and natural gas production accounts for almost 50% of GDP, substantial earnings are also generated from overseas investments. Despite its large wealth, much of Brunei remains underdeveloped. Major economic reforms are required to diversify beyond oil and gas production, including the development of new industries, such as tourism. There was little or no variation in the economic risk rating prior to 1993. Economic growth fell

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signiﬁcantly after 1986 due to low oil prices and voluntary cuts in domestic oil production. From 1993 to 1997, the economic rating had a rising trend, with two sharp falls in 1996 and 1997, rose quickly in 1997 and fell again to 1999, after which it followed a generally increasing trend. The Asian crises in 1997 –1998, as well as large ﬂuctuations in oil prices, led to increased economic uncertainty and instability. Moreover, the 1998 collapse of Prince Jefri’s Amedeo Development Corporation, the country’s largest construction company, led to massive debts and a mild economic recession. These events are reﬂected in the numerous volatility peaks from 1996 to 2000. Brunei is a low ﬁnancial risk country, with a ﬁnancial risk rating varying from 92 to 99. As for the economic risk rating, the Asian crises of 1997– 1998 led to large ﬂuctuations and high volatility in ﬁnancial risk rating. The ﬁnancial rating rose after 1997, with little associated volatility. Brunei became independent in 1984 and has been ruled by the same family for over six centuries. Two political parties were legalized in 1985– 1986, but one was dissolved 13 years later, while the other remains inactive. Prior to 1997, political risk rating had a rising trend, after which it fell substantially in response to the instability caused by the Asian crises and the collapse of the Amedeo conglomerate. The political rating followed a rising trend from 1998 to 2002, with virtually no volatility, apart from three peaks. Overall, the composite risk rating reﬂects the trends and volatilities in the economic, ﬁnancial and political risk ratings. Figure 4.7 gives the four risk ratings for China, the world’s most populous country. In 1949, the Communist Party leader, Mao Zedong, founded the People’s Republic of China and led the country for three decades under strict economic and political controls. After gaining power in 1978, Mao’s successor, Deng Xiaoping, gradually introduced market reforms, decentralized economic decision-making, and consolidated his authority. China switched from collective agriculture to small-scale enterprises in services and manufacturing, opened the economy to foreign trade and investment, quadrupled GDP by 2000, and became the world’s second largest economy by 2002. The economic risk rating was volatile and had a declining trend to late 1989 as the economy overheated and inﬂation rose. Economic activity expanded in the early 1990s, with the rating rising to 84 by late 1992 owing to a renewed drive for market reforms and the creation of a socialist market economy. Contractionary policies in 1993 led to an economic slowdown and a fall in the rating until early 1995. The rating rose until 1997 as inﬂation fell, after which it followed no trend around the 1980s with lower volatility. With continued economic growth and liberalization, the income disparity rose between rural and urban areas. Reforms are needed in the obsolete state-owned

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industries and the ﬁnancial sector. The strong economic performance has lowered ﬁnancial risk in China, with the rating falling until 1992, a generally increasing trend thereafter, greater volatility prior to 1992, and with one peak in 1997. A falling political risk rating until 1990 was followed by a rise to 1993, and then a decline, with a noticeable clustering of volatility in the sample. The risk rating fell in 1989, with a volatility peak, when the Tiananmen Square demonstrations by students, intellectuals and opponents from urban areas led to military intervention, untold casualties, and international outrage and sanctions. While legal reforms were a priority in the 1990s, human rights were often abused due to ofﬁcial intolerance of dissent and inadequate legal protection of human rights. WTO membership in 2001 renewed pressure on the hybrid system of strong political controls and a growing market system. Tibet remains a controversial issue, with China accused of systematic destruction of Tibetan Buddhist culture and persecution of monks loyal to the Dalai Lama, the exiled leader campaigning for autonomy within China. Overall, the composite risk rating reﬂects the trends and volatility in the three component risk ratings. The four risk ratings in Figure 4.8 are for Hong Kong, one of the world’s most open and dynamic economies. Following the 1984 agreement with the UK, Hong Kong reverted to China in 1997 as a Special Administrative Region. The economy depends heavily on international trade, with both imports and exports exceeding GDP, high-accumulated public and private wealth from sustained growth, sound banking system, absence of public debt, strong legal system and rigorous anti-corruption regime. Throughout the sample the economic risk rating had a slight upward trend, with low associated volatility and peaks in 1984 and 1990. GDP growth averaged 5% in 1989– 1997. A high ﬁscal deﬁcit after the 1997 burst of the property price bubble, a weak trade sector and lower growth due to the Asian downturn of 1997– 1998, led to a lower risk rating. The risk rating rose with economic recovery after 1998, GDP grew at 10% by 2000, fell in 2001 as the global downturn led to poor export revenues and a 0.6% growth rate, and rose by 2002 as the economy improved. Hong Kong has strengthened its role as a commercial and trade centre, especially after China’s accession to the WTO in 2001, focusing on ﬁnancial services, logistics, tourism, production and professional services. The ﬁnancial risk rating fell in 1984, rose signiﬁcantly after 1985, fell until 1997 and followed a slight rising trend to 2002, with a noticeable clustering of volatility. Hong Kong’s emergence as a services centre, especially in trade with China, resulted in low ﬁnancial risk after the early 1990s. Under the terms of the 1984 Sino-British Joint Declaration and the ‘one country, two systems’ framework, Hong Kong has retained its political, economic and

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judicial systems for 50 years after the 1997 reversion to Chinese sovereignty. The political risk rating ﬁrst fell and then rose under the British rule, fell again after 1997, and had a generally increasing trend after 1999, with high associated volatility from 1989 to 2001. Hong Kong remains a free and open society, respects the rule of law and human rights, and is a separate and active member of the WTO and the Asia Paciﬁc Economic Cooperation forum. Foreign affairs and defence remain the responsibility of China. In general, the composite risk rating reﬂects the trends and volatility in the three risk rating components. Figure 4.9 presents the four risk ratings for Indonesia, which has a market-based economy with signiﬁcant government participation and ownership. Indonesia has experienced unprecedented turmoil since 1997 due to the South-East Asian ﬁnancial crisis, the fall of President Suharto after 32 years, the ﬁrst free elections since the 1960s, the loss of East Timor, independence demands from restive provinces, bloody inter-ethnic and religious conﬂicts, and unending corruption scandals. Political and judicial reforms and the restructuring of the banking sector and offshore debt are crucial steps towards economic recovery and growth. The economic risk rating had a slightly increasing trend until 1997, when it fell due to the severe ﬁnancial crisis, varied around the 40s until 2000, when it increased by almost 30 points, and was ﬂat at 70 until 2002, with discernable volatility from 1996 to 2000. Prior to 1997, the government removed most regulatory obstacles to the external and ﬁnancial sectors, which led to increased employment, higher growth in the non-oil export sector, and a growth rate of 7% from 1987 to 1997. While economic recovery after the crisis has been slow, consumer conﬁdence and exports increased and the rupiah was stabilized. Indonesia had a low ﬁnancial risk rating until 1988, after which it increased substantially, remained in the high 80s until 1992, followed a downward trend until the 1997 crisis, fell by almost 40 points until 1998, increased in 1999 and remained highly volatile until 2002. While the rupiah has stabilized since 2001, the investment climate has remained troubled due to the lack of legal protection, confusion over regional autonomy policies and ﬁscal decentralization, uneven implementation of economic reforms, and tax and labour issues. The political scene has also been volatile, with the risk rating improving substantially from 1988 to 1997, after which it fell and remained low, but with high variation. Such a fall in the risk rating was due to Soeharto, who presided over 32 years of authoritarian rule, having been forced out in May 1998, amid deepening economic, ﬁnancial and social crises. In 2001, there was a peak in volatility when President Wahid was impeached due to competence and replaced by President Soekarnoputri. As a weighted average of the three component risk ratings, the composite

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risk rating reﬂects the trends and volatility in the ﬁnancial and political risk ratings. The four risk ratings for Japan are given in Figure 4.10. Japan has long been the second largest economy in the world, with one of the highest economic growth rates during 1960– 1980. Despite advanced technology, Japan is still a traditional society with strong social and employment hierarchies. The electronics and automotive industries dominate the manufacturing sector, and have successfully penetrated international markets. While deregulation and liberalization are important structural reforms, the pace of change has been slow. A rapidly ageing population has large effects on the structure of the labour force, savings rate and government budget. The economy slowed dramatically in the early 1990s when the asset bubble collapsed, and entered a severe recession in 1997, which caused a sharp fall in the economic risk rating in 1997, prior to which it decreased and then increased. The risk rating continued to decrease until the end of the sample, with an associated increase in volatility, as Japan experienced its worst period of growth since WWII. There was no trend in the ﬁnancial risk rating with discernable volatility after 1997, prior to which there was no volatility, apart from ﬁve peaks. The political risk rating had a slightly decreasing trend until 1992, when bribery scandals and the recession led to the ﬁrst loss of power for the Liberal Democratic Party since 1955. In 1993 the elections led to a sevenparty coalition, which collapsed in 1994, when an administration supported by the LDP and Socialists gained power. During this period, the political risk rating increased and then decreased, after which it followed a generally increasing trend until 1997, when the economy entered a severe recession. In 1998, when Keizo Obuchi of the LDP became prime minister, the political risk rating started to increase, which ended in 2001. Despite the current economic difﬁculties, Japan remains a major economic power. Foreign policy aims to promote peace and prosperity for Japan by working closely with the West and supporting the UN. While maintaining its strong relationship with the USA, Japan has diversiﬁed and expanded ties with other nations, especially its Asian neighbours. Overall, the composite risk rating for Japan reﬂects the trends in the three component risk ratings, but is less volatile than the political risk rating, in general, and the ﬁnancial risk rating after 1997. Figure 4.11 presents the four risk ratings for Malaysia. After independence in 1957, the economy was based on two commodities, rubber and tin, but the economic performance was impressive for the following 40 years. New foreign and domestic investments from the early 1980s to mid-1990s played a signiﬁcant role in transforming the economy from a commodity based to a manufacturing system. Although one of the

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world’s largest exporters of semiconductors and targeted toward being a leading producer and developer of high-tech products, the sustained high growth ended with the Asian crisis in 1997. The economic risk rating followed an increasing trend until the economic and ﬁnancial crises in 1997, after which it fell, reaching the low 60s in 1998, with an associated peak in volatility. During 1998, the government focused on expansionary measures to deal with the crises. A range of capital controls was implemented to restrict the ﬂow of capital in and out of Malaysia, and the ringgit was pegged against the US dollar. The economic risk rating increased from 1999 to 2000 as capital controls were eased to restore foreign investor conﬁdence and the economy recovered. A downward trend in the rating after 2000 reﬂected the impact of the global downturn on exports and economic growth. The ﬁnancial risk rating was also affected by the crises in 1997. However, the rating started to increase in the same year, with an associated peak in volatility, and remained ﬂat after 1999. Malaysia is a multi-ethnic federation, with the Malay community beneﬁting from positive discrimination in business, education and the civil service. However, the ethnic Chinese hold economic power and are the wealthiest community. A decreasing trend in the political risk rating to 1988 was followed by an increasing trend until the 1997 Asian crisis, after which the risk rating fell and followed a slight upward trend from 1998, with discernable volatility throughout the sample. A serious challenge remains to sustain political stability amid the economic downturn and the ethnic wealth gap. As a founding member of ASEAN, Malaysia views regional cooperation as the basis for its foreign policy. Overall, the composite risk rating closely reﬂects the trends in the ﬁnancial and political risk ratings, and is less volatile than the political risk rating. Figure 4.12 plots the risk ratings, risk returns and sample volatilities for Mongolia. Famous for the 13th century conquests under Genghis Khan, Mongolia won its independence from Chinese rule in 1921. A Soviet-style, one ruling party state was implemented in 1924, but was abandoned 70 years later in favour of political and economic reforms. However, the rapid reforms of 1990 – 1991 for a new market economy were disrupted by the collapse of the former Soviet Union. The withdrawal of Soviet support led to widespread poverty and threatened political stability. Economic activity is based on agriculture and livestock, while extensive mineral deposits account for a large part of industrial production. After the late 1980s, Mongolia strengthened its ﬁnancial and political ties with the USA, Japan and the EU, which led to the creation of a tourism sector. However, trade and economic development still remain heavily dependent on its two neighbours, Russia and China. Given these events, the economic risk rating was ﬂat until 1990, with little or no volatility, apart from a signiﬁcant fall

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in 1990. However, the rating increased from 1990 to early 1992, after which it fell by almost 30 and remained ﬂat to early 1996. During this period, Mongolia experienced triple-digit inﬂation, rising unemployment, a series of natural disasters, and hence decreasing economic output. Economic activity improved after 1996, with the economic risk rating rising by 30 in 1996 and remaining high until 1997. Following the 1997 South-East Asian economic and ﬁnancial crises, the 1999 collapse of the Russian ruble, and the worsening of commodity prices, the rating fell and had no trend to 2002, with noticeable volatility peaks, especially in 1996 and 1997. Like the economic risk rating, the ﬁnancial risk rating fell substantially from 1991 to 1992, and remained low until 1998, after which it increased and followed no trend to 2002. There is little or no volatility, apart for the period 1997– 1999. Unlike the economic and ﬁnancial risk ratings, the political risk rating followed an increasing trend, with noticeable volatility for 1990 – 1993 and 1996 – 2002. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. The four risk ratings for New Zealand in Figure 4.13 reﬂect a small economy, heavily reliant on commodity production in agriculture, ﬁshing and forestry, to provide a substantial share of export earnings and supply inputs for processing industries. A recent shift towards further processing of primary commodities reﬂects unstable world commodity prices. Since the UK entered the EU, new markets have been developed, including tourism. In 1984, controversial economic reforms were launched in response to increasing external debt, leading to the removal of government subsidies, tariffs and controls on interest rates, wages and prices, import liberalization, freely ﬂoating exchange rate and tax reduction. A smaller budget deﬁcit lowered inﬂation, and the restructuring and sale of government-owned enterprises in the 1990s reduced the public sector role in the economy. Consequently, the economic risk rating followed a generally increasing trend throughout the sample, associated with high volatility. There was a structural change in the ﬁnancial risk rating in 1997, which fell by more than 20 points, prior to which it ranged from the mid80s to low 90s, with little volatility. Financial risk increased after 1997, exhibiting greater volatility around a level of 60. For the political rating, an absence of trend until 1988 was followed by a steep decreasing trend to 1990 and a generally upward trend to 2002, with a noticeable clustering of volatility. Political risk was low but increased substantially under the 1984 Labour government, which enacted an anti-nuclear policy and led to the suspension of New Zealand from the ANZUS security alliance with the USA and Australia. The National Party won the 1990 election, a new proportional electoral system increased the representation of smaller

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parties in Parliament, with the number of Maori MPs increasing from 6 to 15 in 1996, and the Labour Party won the 1999 election. Foreign policy is oriented toward developed democratic nations and emerging Paciﬁc economies. Despite the rupture in the ANZUS alliance, a good relationship has been maintained with the USA and Australia on a broad range of international issues. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.14 presents the four risk ratings for North Korea, one of the world’s most centrally planned and isolated economies. Founding President Kim Il-sung, who dominated economic and political affairs for almost half a century, was succeeded in 1997 by his son, Kim Jong-il. Decades of economic mismanagement and state control led to low industrial capital stock and output, stagnation and heavy reliance on international food aid, while large-scale military spending directed resources from investment and consumption. The economic risk rating started at 56 and fell to a low of 14 by 1999, with mild associated volatility. After 1984, there was an emphasis on expanding trade, attracting foreign capital and acquiring technology, but problems with infrastructure, bureaucracy, and uncertainty in investment and its viability hindered growth and development. Economic ties with South Korea improved after 1988 as imports increased from North Korea, but the fall of Communism in Eastern Europe and the former Soviet Union led to a breakdown in trade with these regions. Unlike other centrally planned economies, the government retained tight political control, and failed to embark on economic reforms and liberalization of trade and investment. After massive international food aid in 1990 –2000, the rating rose by almost 30 points, but was ﬂat in the mid-30s until 2002, with no volatility, apart from a peak in 2000. The ﬁnancial risk rating was also consistently low, falling from high 50s in 1984 to high 20s by 1993, rising then falling to 1997, and following a slight rising trend to high 30s until 2002, with noticeable volatility peaks. Little is known about the line of political authority, apart from the formal constitutional structure. The political risk rating fell until 1997 and rose to 2000, with associated volatility and a peak in 1991. Seoul’s ‘sunshine policy’ of dialogue and aid, and the 2000 visit of President, Kim Dae-jung, strengthened relations between the two countries. However, the risk rating fell after early 2001, with international concern about North Korea’s long-range missile development, research in nuclear, chemical, and biological weapons, and massive armed forces. In 2002, the leadership was outraged by US President George W Bush’s description of North Korea as part of an ‘axis of evil’. Overall, the

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composite risk rating is very low and reﬂects the trends and volatility in the three component risk ratings. Figure 4.15 presents the four risk ratings, risk returns and volatility for Papua New Guinea (PNG), which forms the eastern half of the world’s second largest island, New Guinea. PNG is rich in natural resources, such as oil, copper and gold, which account for 72% of exports. However, the difﬁcult terrain and lack of infrastructure have placed severe limits on the exploitation of these resources. Two types of economies exist side by side, namely the traditional and modern economies. The traditional economy is based on subsistence agriculture, and supports about 75% of the population, while the modern economy, which is heavily dependent on exports, supports the remainder. Australia, Japan and the EU are PNG’s major export markets and primary sources of aid. However, since independence in 1975, the economic, ﬁnancial and political aspects of PNG have been dominated by a 9-year secessionist revolt on the island of Bougainville, which ended in 1998. After a period with no trend, the economic risk rating fell by around 20 in early 1990 and grew steadily to 1996. The economic rating had a falling trend to 2002, with little volatility and a peak in early 1990. Unlike the economic risk rating, the ﬁnancial rating for PNG had a falling trend to 1990, followed by no change to 1993 and a rising trend to 2002, with substantial volatility, especially after mid1997. PNG is a risky country, with the political risk rating ranging from the high 60s in 1984 to mid-50s in 2002. The separatist conﬂict on Bougainville from 1990 to 1998 resulted in a trade blockade, brutal violence and the deaths of thousands. A separatist struggle in the Indonesian province of Irian Jaya, which was renamed Papua in 2000, led to the ﬂight of thousands of Papuans to PNG after the mid-1980s. In view of these events, the political risk rating fell by almost 15 points from 1984 to 1990, with little volatility, apart from a peak in early 1990. Surprisingly, the political risk rating rose from 1990 to 1996, but fell by almost 10 to early 1997. The rating rose quickly in 1997, but thereafter had a falling trend. Apart from 1984 to 1990, there are noticeable volatility peaks and some volatility clustering. Overall, the trends and volatility in the composite risk rating reﬂect those in the political and economic risk ratings. Figure 4.16 gives the four risk ratings for the Philippines, which changed from one of the richest countries in Asia after WWII to one of the poorest. The rule of Ferdinand Marcos from 1965 to 1986 was characterized by economic mismanagement, corruption and martial law. Democracy was re-established by Corazon Aquino from 1986 to 1992, and maintained under Fidel Ramos from 1992 to 1998 and Joseph Estrada from 1998 to 2001. The economy is diversiﬁed and marked by great disparities

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in the ownership of assets, income, levels of technology in production and geographical concentration. There was a generally increasing trend in the economic risk rating, with discernable volatility and a peak in 2001. A recession in 1984 – 1985 shrank the economy by more than 10%. Perceptions of political instability during the Aquino administration dampened economic activity and decreased the economic risk rating. However, economic reforms launched by Ramos led to higher foreign investment, business growth, economic expansion and an increasing rating. The Asian crisis triggered in 1997 and poor weather conditions slowed economic development and decreased the rating again. Economic growth recovered in 1999 – 2000, but the global slowdown depressed export performance until 2002. Reforms are needed to improve infrastructure and the tax system, deregulate and privatize the economy, and increase trade integration with the region. The ﬁnancial risk rating followed a rising trend, with a noticeable clustering of volatility. Foreign debt, which improved substantially before the Asian crisis, deteriorated after the 1997 depreciation of the peso. The government aim is to remedy budget imbalances and restore domestic and international business conﬁdence, which collapsed in late 2000. A rising trend in the political risk rating to 1997 was followed by a declining trend to 2000, after which there was a slight improvement, with discernable volatility throughout the sample. There was an increase in the political risk rating in 1992 and an associated volatility peak, but it fell in 1997. Allegations of corruption, betrayal of public trust, and violation of the constitution led to Estrada’s replacement by his Vice President, Gloria Arroyo, in 2001. Political stability and the ongoing Muslim insurgencies in the south remain major concerns. Generally, the composite risk rating reﬂects the trends and volatility in the three component risk ratings. Figure 4.17 gives the four risk ratings for Singapore, a small, highly successful free-market economy, with an open environment, stable prices and extremely high GDP per capita. After independence in 1965, Singapore adopted a pro-business, foreign investment and export orientation, combined with state-directed investments in strategic government-owned corporations. This resulted in an impressive economic performance, with real growth averaging 8% from 1960 to 1999. There was an increasing trend in the economic risk rating throughout the sample, with higher volatility after 1992 and a peak in 1999. The rating fell markedly from 1997 to early 1999 due to the Asian crisis, but then rose quickly to 100 by 2000 after government attempts to reduce business costs, with a GDP growth rate of 9.4% in 2000. Exports in electronics and manufacturing suffered in 2001 due to the global recession and the sharp slump in the technology sector, which resulted in GDP contraction and

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a fall in the rating. Economic conditions and the risk rating remained unstable, even though growth recovered in 2002, reﬂecting a need to launch a new growth path less vulnerable to the external business cycle than the export-led model. The ﬁnancial risk rating fell until 1986, rose until 1990, was above 95 until 1997, when the rating fell, increased to 1998, and continued in the low 90s until 2002, with discernable volatility prior to 1992 and after 1997. Singapore is an established ﬁnancial and high-tech centre, and its low ﬁnancial risk, corruption-free government, skilled work force, and advanced and efﬁcient infrastructure have attracted investments from more than 3000 corporations from the USA, Japan and Europe. The political risk rating fell until 1986, was ﬂat to 1994, and followed a generally increasing trend to 2002, with discernable volatility throughout the sample. Although a multiparty nation, the People’s Action Party has been the dominant political force since independence. Goh Chok Tong succeeded Lee Kuan Yew, Prime Minister until 1990 and now the Senior Minister. Singapore supports the concept of Southeast Asian regionalism. In 2001, Malaysia and Singapore agreed to end a series of long-standing disputes ranging from water supplies to air space, build a new bridge and tunnel, and demolish the causeway between the two countries. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.18 presents the four risk ratings for South Korea. There has been spectacular growth over the past 30 years, with GNP per capita rising from USD100 in 1963 to USD9800 in 2002. In the 1970s, there was a shift towards heavy and chemical industries, consumer electronics and automobiles. Manufacturing grew rapidly in the 1980s and 1990s, and the economic risk rating increased with little volatility until 1997. From 1997 to 1998, the country experienced economic and ﬁnancial crises, causing the rating to fall by more than 20 points, with a volatility peak. The economy recovered quickly due to IMF assistance and extensive economic and ﬁnancial reforms, which restored stability to markets and maintained high growth. Consequently, the economic risk rating increased in late 1998 with a volatility peak, reaching the high 80s by late 1999, after which the rating followed a generally decreasing trend. Financial risk fell until 1988 and remained low from 1988 to 1997, with little or no volatility in the rating, which followed a generally upward trend. There was a volatility peak in 1997, when the rating fell by almost 35 points, as the country was hit by economic and ﬁnancial crises. However, reforms caused the ﬁnancial risk rating to increase in 1998, after which it remained in the high 70s with some volatility. Unﬁnished reform tasks include the restructuring of Korean conglomerates (chaebols), bank privatization and economic liberalization with a mechanism for winding up bankrupt ﬁrms. Support for

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democracy was strengthened after the Kwangju confrontation in 1980, leading to the ﬁrst democratic election in 1987. There was a structural change in the political risk rating in 1991 when both North and South Korea joined the UN, prior to which the rating was low and had no trend but some volatility. The structural change caused the rating to increase by almost 15 points with a volatility peak, after which it remained high with an associated clustering of volatility. The major political concern for South Korea is North Korea. Relations between the two countries improved after the 1997 election of Kim Dae-jung, when the ‘Sunshine Policy’ led to the historic June 2000 Inter-Korean summit. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the other three risk ratings. The four risk ratings in Figure 4.19 are for Taiwan, one of Asia’s largest traders. Following the 1949 Communist victory in China, 2 million Nationalists ﬂed to Taiwan, established a democratic government and control over the indigenous population. China insists that no country can have formal ties with both China and the renegade province of Taiwan. After decades of sound economic management, Taiwan has been transformed to an economic power, a leading producer of high-technology products, and a creditor economy, holding one of the world’s largest foreign exchange reserves. Given its export-oriented nature, with electronics being the most important industrial sector, Taiwan is vulnerable to external economic downturns. Owing to sustained economic growth, full employment and low inﬂation, the economic risk rating had a slight increasing trend to 2001, with mild volatility, apart from a peak in 1996. Taiwan suffered less than its Asian neighbours from the crisis in 1998– 1999 because of its conservative ﬁnancial approach and entrepreneurial strengths. However, the global slowdown, poor policy coordination by the new government, and increasing debts in the banking sector, led to a recession in 2001, the ﬁrst whole year of negative growth since 1947, a fall in the economic rating, and an associated volatility peak. In 2002, Taiwan became a member of the WTO, economic performance improved, and the rating increased. The ﬁnancial risk rating rose until 1988, remained above 95 until 1997 when the rating fell by more than 15 points, and had little associated volatility prior to a peak in 1997. A second volatility peak occurred in 1998, when the rating rose by 10 points, after which it remained around 90 with virtually no volatility. There is no trend but a noticeable clustering of volatility in the political risk rating. The rating fell in 1990 as the ﬁrst native-born president was elected, his 1996 re-election led to a rise in the rating and an associated volatility peak, and the ﬁrst peaceful transfer of power from the Nationalists to the Democratic Progressive Party led to a rise in the rating in 2000. Domestic political

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reforms and the controversial process of uniﬁcation with China remain dominant and controversial political issues. Overall, the composite risk rating reﬂects the trends and the volatility in the three component risk ratings. The four risk ratings in Figure 4.20 are for Thailand, one of the most diverse economies in South East Asia in recent years, with the world’s highest growth rate from 1985 to 1995 at 9.4%. Large and inexpensive labour and natural resources, ﬁscal conservatism, foreign investment promotion, increasing private sector, strong export-oriented manufacturing sector, and fast growing higher technology sector underscored economic success to 1997. Increasing speculative pressure in 1997 led to a currency crisis, uncovered weaknesses in the ﬁnancial sector, and forced the government to ﬂoat the baht. Long pegged to a strong US dollar, the baht hit its lowest point and the economy contracted by 10.2% in 1998. An economic recovery started in 1999, and strong exports led to a 4.4% growth in 2000, but a frail ﬁnancial sector, slow corporate debt restructuring, and weaker global demand for exports led to lower growth in 2001 – 2002. Reforms are needed to diversify export markets, substitute imports with local goods and enhance economic stability. Generally, the economic and ﬁnancial risk ratings rose to 1996 and 1997, respectively, after which they fell due to the crisis. Both rose in 1998, after which the economic risk rating had a declining trend due to the unstable economy, while the ﬁnancial risk rating had a slight rising trend as the central bank shifted its focus from inﬂation to exchange rate stability. Prior to 1997, the volatility was lower for the economic risk rating and mild for the ﬁnancial risk rating, after which economic volatility rose substantially, whereas ﬁnancial volatility remained low, apart from a peak in 1997. The political risk rating had a rising trend, with discernable volatility to 2002. Since the abolition of absolute monarchy in 1932, there have been 17 military coups, the last in 1991. Civilian governments have been brief and unstable, such as that of Chavalit Yongchaiyudh, which was formed in 1996 and lasted until late 1997. The onset of the Asian crisis caused a loss of conﬁdence in the government and forced Chavalit to hand power to Chuan Leekpai, who formed a coalition government emphasizing prudent economic management and political reforms mandated by the 1997 Constitution. The 2001 democratic elections resulted in the victory of the Thai Rak Thai Party. Overall, the composite risk rating reﬂects the trends in the three component risk ratings, and is less volatile than the economic and political risk ratings. Figure 4.21 presents the risk ratings, risk returns and volatility for the Socialist Republic of Vietnam, which was formed in 1976 after Communist North Vietnam seized control of South Vietnam in 1975. Vietnam’s uniﬁcation was preceded by three decades of conﬂict by

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the Communists against France (1945– 1954) and the US-backed South Vietnam (1963– 1975). The US involvement in hostilities resulted in ferocious jungle warfare, with heavy casualties, and destruction and contamination of the landscape. President Clinton’s visit to Vietnam in 2000 represented an improvement in relations between the two countries. Vietnam faced serious economic problems after uniﬁcation, with failed initial attempts to develop the economy along central planning lines. However, following Congressional approval of major economic reforms in 1986, Vietnam moved from a centrally planned to a multi-sectoral market economy. After 1989, the economic risk rating had a generally rising trend, apart from 1997 to 1998, when it fell due to the Asian economic and ﬁnancial crises. There was little or no associated volatility, with some noticeable peaks. Financial risk rating had a generally rising trend after 1988, with a signiﬁcant fall in 1998 due to the Asian crises and little volatility, apart from peaks during 1991 – 1998. Overall, trends in economic and ﬁnancial risk ratings reﬂect substantial economic progress and an improved business climate. The growth rate was 8% from 1990 to 1997 and 6.5% from 1998 to 2002, inﬂation fell from 300% in 1987 to an average of 4% after 1997, and investment, savings, agricultural production, foreign trade and foreign direct investment improved signiﬁcantly. While Vietnam moves towards a market-oriented economy, the government continues to control major sectors, such as banking and state-owned enterprises. Unlike the economic and ﬁnancial risk ratings, political risk rating rose to 1996 and had a generally falling trend to 2002, with little volatility and a peak in 1993. Since the Asian crises, there have been concerns that economic liberalization has weakened the power base of the Party and introduced ‘decadent’ ideas. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. 4.3.3. East Europe Figures 4.22– 4.30 present the four risk ratings, risk returns and the associated volatilities for the nine East Europe countries, namely Albania, Bulgaria, Czech Republic, Hungary, Poland, Romania, Russia, Slovak Republic and Yugoslavia. The four risk ratings in Figure 4.22 reﬂect the transition market economy of Albania, which ended 44 years of xenophobic Communist rule in 1990. Moving from a centrally planned to a market system has been difﬁcult due to severe economic, social and political inherited problems. Throughout the sample, the economic, ﬁnancial and political risk ratings followed similar trends, with a discernable clustering of volatility from

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1991 to 2000. The three ratings were low with mild variation until the end of Communist rule. However, changes in the former Communist Block by 1990 also affected Albania, with the collapse in social and economic life. In 1991, clashes between Communists and their opponents led to the fall of the regime, with a sharp fall in the three ratings and associated volatility peaks. The 1992 Democratic Party government, led by President Sali Berisha, launched an ambitious program which resulted in price and exchange rate liberalization, ﬁscal consolidation, monetary restraint, privatization, enterprise and ﬁnancial reform, and high growth. However, progress was stalled in 1997 when several pyramid ﬁnancial schemes collapsed and the risk ratings dropped. The collapse caused panic and led to the fall of the government, with the Socialist Party coming to power in June 1997. After 1998, the economic and ﬁnancial risk ratings rose and remained ﬂat, as the economy recovered from foreign remittances, expansion of the construction and service industries, and an increase in seaside tourism. Major unresolved problems include soaring unemployment, high inﬂation, dilapidated infrastructure, unexploited natural resources, low foreign investment, high trade deﬁcits and inefﬁcient energy production. In an effort to promote regional trade, in 2001 Albania agreed with Macedonia, Bulgaria, Croatia, Yugoslavia and Romania to establish a free trade area in south central Europe by 2004. Political instability was high from 1997 to 2000, after which the political risk rating was consistently ﬂat. While democratic reforms continue, government and judicial corruption, and widespread organized crime remain unchecked. Albania pursues greater Euro-Atlantic integration and restored its relations with Yugoslavia following the ouster of Milosevic, but the status of Kosovo remains a key unresolved issue. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the three component risk ratings. Figure 4.23 gives the four risk ratings for Bulgaria. The economy contracted dramatically after 1989 with the collapse of the Communist system and the loss of the Soviet market. This is reﬂected in the downward trend for the economic risk rating from 1989 to early 1991, when the rating dropped to the low 30s. Subsequently, the economic rating was highly volatile and followed an increasing trend to early 1996, after which the rating fell rapidly due to short-sighted economic reforms and an unstable and decapitalized banking system. The economic risk rating experienced an increasing trend when ambitious reforms were launched by the government in 1997 to bring growth and stability to the economy. As a result, inﬂation fell, investments and exports increased, and GDP rose substantially. The new government in 2001 remained committed to the market reforms initiated in 1997, with the economic risk rating remaining

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stable until 2002. Regarding the ﬁnancial risk rating, Bulgaria exhibited little variation until 1996, with a decreasing trend to 1991 due to ﬁnancial turmoil, followed by an increasing trend. However, with the election of 1997, the ﬁnancial rating increased substantially due to government reforms. In 1999, the rating fell then rose as the government remained committed to developing a market-based economy. The political risk rating varied substantially throughout the sample period, with a clustering of volatility. In 1989, the Communist regime collapsed. By 1990, the political rating had an increasing trend when the ﬁrst pluralist elections were held amid democratic change. This trend ended in late 1995 as Bulgaria experienced economic and ﬁnancial crises. Political risk increased amidst protests over the crises, causing the rating to fall substantially from late 1995 to 1996. However, the opposition boycotted parliament and called for elections in April 1997. Consequently, the political rating increased and remained high until early 1999, after which it had a decreasing trend as the government, led by the Union of Democratic Forces, was losing its popularity due to high corruption and unemployment. With elections in 2001, a new government was elected and the rating started to increase. Finally, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. The four risk ratings for the Czech Republic are given in Figure 4.24. Following Czechoslovakia’s ‘velvet divorce’, the Czech Republic was peacefully founded in January 1993. Of the former Communist countries in Europe, the Czech Republic is one of the most developed and industrialized. Prior to 1997, there were noticeable structural changes in the economic, ﬁnancial and composite risk ratings and volatility, after which the economic risk rating was generally ﬂat. The economy experienced rapid growth, especially during 1994 –1996, as the government priorities were economic reform and privatization of the public sector. However, an increasing current account deﬁcit, slowing consumer demand, rising unemployment and declining export competitiveness led to an economic downturn in 1997. The economic rating continued to decrease until 1999 due to the recession, after which it had an increasing trend and then remained ﬂat as the economy recovered. Rising foreign direct investment and further economic reforms led to an improvement in economic risk. Up to 1997, the ﬁnancial risk rating had an increasing trend, reaching a level of 90 in mid-1997 due to ﬁnancial reforms and banking privatization. The rating fell by more than 20 points in 1997, after which it increased and was generally ﬂat thereafter. Following the recession in 1998– 1999, the government was committed to ﬁnancial reforms and banking privatization. Political risk decreased until 1997 but the political scene changed as the country entered a recession. This was reﬂected in an

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increasing political rating trend, followed by a generally decreasing trend with an associated clustering of volatility. Elections in 1998 and full membership of NATO in March 1999 had no impact on the political rating, which remained ﬂat before falling in mid-1999. The government revised its stand towards the rights of the Roma community and an intended nuclear power plant, which threatened accession to the EU. Following street protests in early 2001 and a strike by journalists, the rating increased and then remained ﬂat. Overall, country risk for the Czech Republic increased after 1997 when the composite rating decreased by almost 10 points. Trends and volatility in the composite risk rating reﬂect those of the ﬁnancial and political risk ratings. Figure 4.25 presents the four risk ratings for Hungary, one of the most open economies in Eastern Europe, with signiﬁcant ties to Western Europe through trade and foreign investment. The economic risk rating fell until the end of the Communist era in 1989. However, following economic reforms in 1990, the risk rating rose, with decreasing volatility. In late 1993, the rating fell as Hungary experienced large government deﬁcits, rising inﬂation, increasing unemployment and high external debt, and GDP fell due to slow privatization, lower industrial output and lower exports to the former Soviet bloc. The rating increased substantially in 1996, with a peak in volatility, as the new government introduced an austerity program, an aggressive privatization policy, and export-promoting exchange rate regime. Consequently, Hungary experienced lower economic risk after 1997 and was invited to join the EU in 2004. There was a generally decreasing trend in the ﬁnancial risk rating until 1987 due to the failing Communist system, but the introduction of market-based banking after 1990 resulted in a generally upward trend. However, the rating fell by about 15 points in 1997, and exhibited no trend and little volatility, when the government stopped the privatization program and renationalized the postal savings bank. After the creation of the Hungarian Democratic Forum in 1988 and the dismantling of the Communist state in 1989, Hungary started a transition to democracy. Owing to these political changes, the political rating fell from 1984 to 1991, with increasing volatility. The ﬁrst free elections in 1990 had a positive effect on the political rating, and the rating increased steadily from late 1991 to late 1998. The return to power of the former Communists in 1994 only had a slight negative effect on the political rating. However, political risk increased from 1998 as the new government formed by the Federation of Young Democrats did little to address structural problems, corruption, money laundering and organized crime. The political risk started to fall after 2000, with the elections in 2001 bringing to power a new centre-left coalition and a positive effect on the rating. The composite risk rating fell

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until early 1991, followed by an upward trend until 1996 and a slight downward trend thereafter, with a noticeable clustering of volatility. The four risk ratings in Figure 4.26 are for Poland, the ﬁrst Eastern European country to overthrow Communist rule in 1989 and the largest candidate country to join the EU in 2004. Since 1990, Poland has progressed toward a market-oriented economy and a democratic government, but there is still high unemployment, low income and growth, a large but inefﬁcient farming sector, excessive red tape and a high level of political inﬂuence in business decisions. The four risk ratings exhibit similar trends, being very low until 1989, increasing substantially to 1997, and having no trend to 2002. There is a noticeable clustering in volatility of the four ratings, with greater volatility prior to 1992. The low economic and ﬁnancial risk ratings until 1989 reﬂected a declining economy under a failing Communist system. Trends changed for the economic and ﬁnancial ratings arising from the 1990 economic reforms, which removed price controls and subsidies to industry, opened markets to international competition, imposed strict budgetary and monetary discipline, and launched high-scale privatization. The economy had an accelerated recovery after 1992, with an expanding private sector and rising foreign direct investment. Poland has close trading links with the EU, and Germany in particular, and fosters regional integration and trade through the Central European Free Trade Agreement, which includes Hungary, Czech and Slovak Republics and Slovenia. As the economy slowed in recent years, the economic and ﬁnancial ratings remained ﬂat and varied around 70 and 80, respectively. The Soviet-backed Communist regime weakened economically in the 1970s. A peaceful national revolt led to the creation of the Solidarity trade union movement in 1980. The government imposed martial law in 1982 but was forced to agree to elections in 1989, which led to the formation of the ﬁrst non-communist government in Eastern Europe. A steadily increasing political rating followed the progression toward democracy, but a downward trend after 1999 when Poland joined NATO was followed by a recovery in 2001, remaining in the high 70s. As a weighted average of the three risk components, the composite risk rating closely reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. The four risk ratings in Figure 4.27 reﬂect Romania, with rich agricultural lands, diverse energy sources, large manufacturing industrial base, an educated and well-trained labour force, and great potential for tourism in the Black Sea and the mountains. Romania has moved slowly from its Communist past relative to its Eastern European neighbours. Despite the 1989 uprising that ended the severe rule of President Nicolae Ceausescu, the former Communists remain a dominant force in national

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politics. Since the change in regime, successive governments have tried to build a Western style market economy. The pace of restructuring has been slow due to a persistent failure to undertake structural reforms, with periods of high growth followed by high inﬂation and economic imbalance. Further ﬁscal consolidation, restructuring and privatization of large state enterprises and utilities remain major economic policy issues. The economic risk rating had a declining trend to 1991, after which it followed a generally increasing trend, with greater volatility. As reﬂected in the falling trend, Romania entered a period of deep recession in mid1997 mainly due to industrial restructuring. Economic recovery started in 1999 and, though inﬂation remained high, foreign investment increased slowly and trade with the West also grew. The ﬁnancial and political risk ratings decreased until the fall of the regime in the late 1980s and followed increasing trends until 1997, with noticeable volatility, especially for the political risk rating. There was a fall in the ﬁnancial risk rating in 1997 as a result of the recession, after which it was unstable until early 2000, with a noticeable volatility clustering from mid-1997 to early 2001 owing to foreign exchange shortages and a poorly developed ﬁnancial sector. Two major political changes occurred in Romania after 1989 which led to downward movements in the political risk rating and increasing volatility, namely the electoral defeat in 1996 of the former Communists, who came to power after Ceausescu’s demise, and the 2000 elections, which saw the former Communists returned to power. Under new President Illiescu, the political situation has stabilized, as shown by the ﬂat political risk rating, and low associated volatility. Romania aims to strengthen relations with the West, particularly the USA and EU. Generally, the composite risk rating closely reﬂects the trends and volatility in the ﬁnancial and political risk ratings. Figure 4.28 presents the four risk ratings for Russia, which has had to overcome the legacy of Communism since the collapse of the Soviet Union in 1991. Economic risk was very high as the transition from a centrally planned to a free market economic system started. The economic risk rating had an upward trend from mid-1992, was in the high 80s until early 1996, fell to a low of 32 in 1998, increased substantially to 2000, and had no trend to 2002, with a noticeable clustering of volatility and a peak in 1998. Slow reforms after 1992, short-term borrowings to ﬁnance budget deﬁcits, low prices for major exports (oil, gas and minerals), and loss of investor conﬁdence after the Asian ﬁnancial crisis led to the collapse of the rouble, ﬂight of foreign investment, default on foreign debt payments, a breakdown in the banking system and severe inﬂation. Economic risk fell after 1998 as Russia recovered from the crisis, real GDP grew strongly, the Rouble stabilized, inﬂation fell, investment rose, foreign debt obligations

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were met, reserves were built up, and the budget, trade and current accounts were in surplus. The ﬁnancial risk rating was also negatively affected by the 1998 crisis, falling by 31 points in 1 year, after which it rose and remained in the high 70s. Prior to the crisis, the ﬁnancial risk rating had little volatility, with a peak in 1998, no trend until 1995, and an upward trend to 1998, despite increasing ﬁnancial problems. A generally upward trend for the political risk rating to 1997 was followed by a downward trend to late 1999 and an upward trend thereafter, with a noticeable clustering of volatility. After the dissolution of the Soviet Union, the Russian Federation became its largest successor state, and inherited a seat on the UN Security Council. Boris Yeltsin won the ﬁrst democratic parliamentary elections in 1991, but 1993 saw the dissolution of parliament, new elections, and a new constitution. In late 1994, Russian troops launched an operation in Chechnya against separatist rebels, with a negative impact on the political risk rating. After many futile attempts, a peace treaty was concluded in 1997. However, political risk rose after 1997 due to internal political instability, and reached a trough in 1999 when Russian troops returned to Chechnya, following the Dagestan attack by Chechen separatists and the bombings of two apartment blocks in Moscow. The political risk rating rose as Putin replaced Yeltsin and was elected president in 2000. A fall occurred in 2000 when the Kursk nuclear submarine sank in the Barents Sea, with all its crew lost. The rating continued to increase as Russia’s supportive policy on the US-led campaign against terrorism after September 11, 2001, led to muted Western criticism over its actions in Chechnya and improved relations with NATO. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. The four risk ratings in Figure 4.29 are for the Slovak Republic, which achieved independence in January 1993 following Czechoslovakia’s ‘velvet divorce’. After 1993, Slovakia experienced a period of rejecting economic reforms and had frosty relations with the EU and NATO under the leadership of Vladimir Meciar. Growth in this period was mainly due to high government spending and over-borrowing to ﬁnance economic activity. A new government in 1998, led by Mikulas Dzurida, steered the country towards EU and NATO integration and accelerated economic restructuring. There was a generally upward trend for the economic risk rating until 1996, followed by a decrease to 1999 and an increase to 2002, with greater volatility prior to 1997. The fundamentals improved after 1994, with real GDP growth peaking at 6.5% in 1995, but public and private debt and trade deﬁcits soared. Crony capitalism affected the privatization process, reﬂecting a declining rating until 1999, with real

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GDP falling to 1.3%. The rating rose after 1999, reaching the high 60s by 2002 as the new government promoted economic reforms guided by EU entry in 2004. However, the reforms were hindered by institutional weaknesses, and unemployment remained among the Europe’s highest. No trend was observed for the ﬁnancial risk rating throughout the sample, as it had an increasing pattern to 1997 and a decreasing pattern to 1999. Due to ﬁnancial problems inherited from the Meciar era, the ﬁnancial risk rating increased after 1999, decreased then increased, and remained at 76 after late 2001. The rating exhibited no volatility during 1993– 1995 and 2001– 2002, but had noticeable volatility otherwise. Slovakia received growing international criticism for respecting neither minority rights nor the democratic process shown by Meciar, who was ousted in parliamentary elections in 1988. Led by Dzurinda, the government’s political reforms enabled Slovakia to enter the OECD and bid for EU and NATO membership. Surprisingly, the political risk rating had a generally upward trend to 1998 and a downward trend after 1999, reﬂecting falling political risk under Meciar and rising political risk under Dzurinda. The composite risk rating reﬂected the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.30 presents the four risk ratings for Yugoslavia. Now known as the Union of Serbia and Montenegro since 2003, it was formerly the Socialist Republic of Yugoslavia, comprising Serbia, Montenegro, Croatia, Slovenia, Bosnia-Herzegovina and Macedonia. Yugoslavia was formed in 1945 under Josip Tito, a devout Communist who settled ethnic tensions. The federation lasted for more than 10 years after Tito’s death, but disintegrated in bloodshed during the 1990s under the Serbian nationalist leader, Slobodan Milosevic, as military efforts sought to create a ‘Greater Serbia’. Slovenia and Macedonia seceded relatively peacefully, but Croatia and Bosnia-Herzegovina had devastating wars. Serbia and Montenegro formed the Federal Republic of Yugoslavia from 1992 to 2003. A military campaign from 1998 against separatist forces in the Serbian province of Kosovo led to NATO’s military response in 1999, when Kosovo became a UN protectorate. The four risk ratings were low throughout the sample as Yugoslavia went through major changes, increasing the economic, ﬁnancial, political and composite risks. As the economy faltered in the 1980s, the economic risk rating fell until 1988 and was ﬂat around 50, with greater volatility to 1992, with a peak in 1997. The ﬁnancial risk rating had no trend, with greater volatility after 1990, fell to the high 20s in 1993, and rose to the low 50s by 2002. Economic mismanagement during the Milosevic era, long lasting economic sanctions, and damage to infrastructure and industry during the Kosovo crisis, reduced the economy to half the size in 1990. Since the downfall of

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Milosevic in October 2000, the Democratic Opposition of Serbia government has embarked on major market reforms, renewed its IMF membership, and rejoined the World Bank and European Bank for Reconstruction and Development, leading to huge ﬂuctuations in the ﬁnancial risk rating. Montenegro was severed from Serbia during the Milosevic era, maintained its central bank, used the Euro as ofﬁcial currency, collected customs tariffs and managed its own budget. With the structural changes, the political risk rating fell in 1992 as Yugoslavia disintegrated, rose until 1997, fell due to the Kosovo conﬂict and NATO attacks in 1999, then increased and remained in the low 50s for 2001– 2002. The risk rating was more volatile after 1991, with two peaks in 1993 and 1999. Overall, the composite risk rating essentially reﬂects the trends in the political risk rating, but with greater volatility. 4.3.4. Middle East and North Africa Figures 4.31– 4.48 present the four risk ratings, risk returns and the associated volatilities for the 18 Middle East and North Africa countries, namely Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates and Yemen. Figure 4.31 presents the four risk ratings, risk returns and their volatility for Algeria, which has experienced violence over the past ﬁve decades. More than 1 million Algerians were killed in the ﬁght for independence from France in 1962, and more than 100,000 were killed in the conﬂicts after the Assembly was dissolved in 1992. The economy is based on the hydrocarbons sector, which accounts for 60% of budget revenues, 30% of GDP and over 95% of export earnings. Although the world’s second largest gas exporter, and rich in oil and natural gas reserves, the fall in oil revenues and high-debt interest payments after the late 1980s led to IMFsupported economic policy reforms and the Paris Club debt rescheduling in the mid-1990s. As a result, Algeria’s ﬁnancial and macroeconomic indicators improved, with an annual growth rate of 2 – 4% since 1999. The economy has beneﬁted from large trade surpluses and foreign exchange reserves from high oil prices in the early 2000s. Overall, the economic and ﬁnancial risk rating had falling trends until 1990, followed by rising trends, reaching the high 70s in 2002. Both ratings varied substantially, with higher volatility in the economic rating after 1998 and lower volatility in the ﬁnancial rating in 1990– 1995. Government efforts to diversify the economy by attracting foreign and domestic investment outside the energy sector have been unsuccessful in reducing unemployment and improving living standards. Banking and judicial reforms, improving the investment

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environment, privatization, and reducing government bureaucracy, remain key issues. Over the sample, the political risk rating had a falling trend, with high 40s in 2002. Politics were dominated by a violent conﬂict between the military and Islamic militants, following the annulment of the 1992 elections that were won by an Islamic party. Security improved after 2000, but issues underlying the political turmoil of the 1990s remain unresolved. Following constitutional amendments in 1988, 1989 and 1996, the Government has strongly supported an open political process and the creation of political institutions. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political ratings. Figure 4.32 plots the risk ratings, risk returns and volatility for Bahrain, one of the ﬁrst countries in the Gulf region to discover oil and build a reﬁnery. The oil sector accounts for about 60% of exports, 60% of government revenues and 30% of GDP. Following the decline in oil reserves, Bahrain has undertaken economic and ﬁnancial diversiﬁcation, and has become a leading international banking and ﬁnancial centre in the Middle East, with the ﬁnancial sector being the second largest contributor to GDP. Service industries, such as information technology, healthcare and education, have been developed, while oil revenues have led to advanced infrastructure in transportation and telecommunications. Regional tourism is also a major source of income. There was no trend in economic risk rating until 1998, after which it fell by more than 25 to the low 60s. The rating rose by 15 in 1999, when the new Amir came to the throne, and remained in the mid-70s to 2002. Little or no volatility was observed over the sample, with two peaks in 1999. Unlike the economic risk rating, ﬁnancial risk rating was low before a structural change in 1992, which led to a rise of almost 30. After 1992, the rating was in the high 80s without trend. Apart from the peak associated with structural change in 1992, there is little volatility in the ﬁnancial risk rating. Given the small size and location among the Persian Gulf countries, Bahrain plays a balancing role in foreign affairs among its larger neighbours. Bahrain has been ruled by the Al Khalifah family since 1783, and was a British protectorate from 1861 until independence in 1971. The new Amir in 1999 pushed for political reforms and improvements in relations with the Shia community. A referendum on the National Action Charter, a political liberalization program, was approved in 2001 and the Amir proclaimed himself king in 2002. Political risk rating rose from low 40s to mid-70s from 1991 to 1993, fell from 1994 to 1996, was in mid-60s to 2000, and rose to high 70s by 2002, with little or no volatility, except for a peak in 1991. Recently, Bahrain has enjoyed greater freedoms and improved human rights. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings.

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Figure 4.33 presents the four risk ratings for Egypt, the second largest economy in the Arab world, with its intellectual and Islamic institutions at the centre of the region’s social and cultural development. The 1979 peace treaty with Israel by President Sadat led to Egypt’s expulsion from the Arab League, which ended in 1989 under President Mubarak. IMF backed reforms were launched in 1991, with signiﬁcant success in tackling the economic problems inherited from the previous decade. The economic risk rating had a declining trend until 1987 and rose to 1994, after which it had a slight declining trend, with greater volatility prior to 1988 and a peak in 1986. Following the 1991 economic reforms, Egypt reduced inﬂation, cut budget deﬁcits and attracted greater foreign investment. Tourism, which is a major sector in the economy, suffered badly after the 1997 Luxor terrorist attacks on 85 tourists. Recently, economic liberalization and privatization have slowed down, and excessive infrastructure spending has led to an increase in the budget deﬁcit. Monetary pressures increased after September 11, 2001 due to falls in tourism, Suez Canal tolls and exports, which are dominated by oil and cotton. The ﬁnancial risk rating was low and had no trend to 1991, rose by almost 40 points to 1992, remained in the high 70s until 1998, and had a declining trend to 2002, with higher volatility prior to 1992. Lower foreign exchange earnings after 1998 resulted in several devaluations of the pound and a falling risk rating. The political risk rating had a falling trend to 1986, and a rising trend to 2002, apart from 1994 to 1996 when it fell, with discernable volatility throughout the sample, and peaks in 1986 and 1991. Egypt is a limited democratic system and has made little progress in political reforms. The executive authority is vested in the president, and the outlawed Muslim Brotherhood is the largest opposition force. Mubarak has been more moderate towards Islamic extremists than his predecessor, as Islamic groups have continued their sporadic campaigns of violence. Given its geography, history, population, military strength and diplomatic expertise, Egypt has extensive political inﬂuence in the Middle East and the Non-aligned Movement, and is a key partner in the resolution of the Israeli – Palestinian conﬂict. In general, the composite risk rating reﬂects the trends in the three component risk ratings, but is less volatile than the ﬁnancial and political risk ratings. The four risk ratings in Figure 4.34 are for Iran, which became an Islamic republic in 1979, when the monarchy was overthrown by religious clerics, led by Ayatollah Khomeini. Assuming political control, they reversed the Shah’s pro-Western policies. The economy is a mixture of central planning, state-owned oil and large enterprises, village agriculture and small-scale private trading and service ventures. Market reforms were initiated by President Rafsanjani in 1989 and supported by his successor, Khatami, in 1997. The economic risk rating had a generally rising trend,

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but fell during 1984 –1989 and 1994– 1999, with a noticeable clustering of volatility. Iran’s 5-year economic plans focus on a gradual diversiﬁcation of the oil-reliant economy. Political and social concerns, and large external debt repayments, limited the policy options in the 1990s. A third 5-year plan in 2000 consisted of an ambitious program of liberalization, diversiﬁcation, privatization and job creation. The ﬁnancial risk rating varied in the 30s until 1989, rose to the mid-80s by early 1999, and was ﬂat to 2002, with a clustering of volatility. A strong oil market in 1996 lessened ﬁnancial pressures and allowed debt service repayments, but the situation worsened in 1997– 1998 due to lower oil prices. Subsequent oil price increases facilitated exchange rate uniﬁcation in early 2002, but political obstacles to rapid reforms remain signiﬁcant. The political risk rating had no trend in the mid-30s until the war with Iraq ended in 1988, rose until 1996 and had a declining trend to 2002, with higher volatility prior to 1992. After 1989, Rafsanjani adopted more moderate policies, and the trend accelerated under Khatami, whose liberal ideas were opposed by the clerics. The victory in 2000 by the liberals over the conservative elite signalled a political and social transformation. Key issues to political stability include the pace of accepting external inﬂuences and reconciliation between the clerics and widespread demands for reform. Iran has attempted to improve relations with its Gulf neighbours, particularly Saudi Arabia. Regional goals are to establish a leadership role, limit the presence of outside powers and build trade links. The US President’s speech in 2002, which described Iraq, Iran and North Korea as an ‘axis of evil’, was condemned by reformists and conservatives alike. Overall, the composite risk rating reﬂects the trends and volatility in the three component risk ratings. The four risk ratings for Iraq in Figure 4.35 are all very low. Although Iraq is an oil-rich country, the war with Iran from 1980 to 1988 seriously damaged its oil export facilities, depleted foreign exchange reserves, devastated the economy and left signiﬁcant foreign debt. Consequently, the economic risk rating was in the mid-20s in 1987, with discernable volatility prior to mid-1987, but followed a rising trend from 1988 to 1991 as new pipelines were constructed, damaged facilities were repaired and oil exports gradually increased. Iraq’s seizure of Kuwait in 1990 and the 1991 war with the US-led UN coalition resulted in international sanctions, which drastically reduced economic activity. During this period, the rating dropped by 30 points, after which it followed an upward trend, with discernable volatility after 1994. A UN oil-for-food program launched in 1996 improved living conditions, and Iraq was authorized in 1991 to export unlimited oil quantities to ﬁnance humanitarian needs, including food, medicine and infrastructure. There was no trend and little variation in

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the ﬁnancial risk rating to 1990, being very low during this period, with a clustering of volatility in 1990– 1991. In the 1980s, ﬁnancial difﬁculties caused by massive expenditures on the war with Iran led to the implementation of austerity measures, heavy borrowing and debt rescheduling. From 1991 to 1994, the ﬁnancial risk rating was virtually ﬂat at 10, after which it had a rising trend associated with increasing volatility. The end of the war with Iran in 1988 saw a rise in the political risk rating, followed by a drop during the Gulf Crisis from 1990 to 1992, a clustering of volatility, and then an increasing trend, with little variation. After the Gulf Crisis, the major issue for Saddam Hussein’s regime was to retain power while overcoming international isolation, and dealing with calls to surrender weapons of mass destruction and submit to UN inspections. The regime refused to cooperate fully with the UN inspectors, and allowed no inspections in Iraq after 1998. Relations with the Arab nations have been unstable, and Iraq has embraced the most extreme antiIsraeli position. In 2002, US President George W. Bush declared Iraq to be part of ‘an axis of evil’, which led to rising tensions in global politics. Overall, the composite risk rating has followed a similar trend to that of the political risk rating, but reﬂects the volatility in all three component risk ratings. Figure 4.36 gives the four risk ratings for Israel, which was established in 1948 after the end of the British mandate of Palestine. The creation of Israel resulted in large masses of displaced Palestinians and bitter conﬂicts among Israelis, Palestinians, and the Arab nations of Egypt, Jordan, Syria and Lebanon. Peace attempts with the Arab nations started in 1979 when Egypt and Israel signed a treaty, but a peace process with the Palestinians began at the 1991 Madrid Conference after a prolonged ‘intifada’. Israel has a diversiﬁed economy with large government ownership, and depends on imports of crude oil, grains, raw materials and military equipment. Due to its high-tech and service sectors, Israel is a world leader in software development and a major tourist destination. The economic risk rating had a rising trend with a noticeable clustering of volatility. A 1985 economic program and the introduction of market-oriented reforms boosted economic performance in the 1990s. Immigration from the former Soviet Union after 1989 and the opening of new markets at the end of the Cold War energized the economy, but setbacks in the peace process, the contraction of the high-tech and tourism sectors, rising inﬂation and ﬁscal austerity measures led to lower growth in 2000 – 2002 and a falling rating. A structural change in the ﬁnancial risk rating in 1991 resulted in an increase of almost 30 points, prior to and after which the rating was ﬂat and varied in the 50s and 80s, respectively, with mild volatility and a peak in 1991. The 1991 peace process ended Israel’s economic isolation and

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increased foreign capital inﬂows. Transfer payments from abroad and foreign loans cover large current account deﬁcits, and almost half of the external debt accrues to the USA, Israel’s major source of economic and military aid. The 1991 peace process led to a structural change in the political risk rating and a rise of almost 35 points. Declining trends were observed prior to and after 1991, with discernable volatility, including two peaks in 1991. Progress towards a ‘ﬁnal status’ agreement was undermined by the Israeli – Palestinian violence after late 2000. Israel seeks acceptance as a sovereign state, with an enhanced international role. Overall, the composite risk reﬂects the trends in the ﬁnancial and political risk ratings and the volatility in all three risk component ratings. The four risk ratings given in Figure 4.37 are for the Kingdom of Jordan, which emerged after the WWI division of the Middle East by Britain and France, and plays a pivotal role in regional conﬂicts. Jordan has limited natural resources, such as water and oil, and serious debt, poverty and unemployment. Economic growth depends on phosphates, agriculture, overseas remittances, tourism and foreign aid. The focus is now on information technology, tariff-free exports and tourism. Until 1999, when it fell by 10 points, the economic risk rating had a generally rising trend, which continued to 2002, with high volatility prior to 1999 and virtually none thereafter. King Hussein’s death in 1999 left the country struggling for economic and social survival. However, in the last 3 years, the government has worked closely with the IMF, practised prudent monetary policy, made signiﬁcant progress with privatization and liberalized trade. In 2000, Jordan joined the WTO, and signed an association agreement with the EU and a free trade accord with the USA, with productivity improvements and greater potential for foreign capital. Current problems include ﬁscal adjustment to reduce the budget deﬁcit and broader investment incentives to promote job-creating ventures. The 1991 agreement to enter peace negotiations with Israel led to structural changes in both the ﬁnancial and political risk ratings. Financial risk fell to 1991, rose by almost 30 points by early 1992, and was ﬂat until 2002, with little volatility and a peak in 1991. King Hussein, who survived many challenges to his rule, led Jordan from 1953 to 1999, drawing on the loyalty of his military and serving as a symbol of unity and stability. The political risk rating increased by almost 30 points in 1991, with a declining trend before 1991 and then a slight upward trend, and little volatility apart from a peak in 1991. King Hussein resumed parliamentary elections in 1989 and gradually permitted political liberalization. Jordan did not participate in the 1990– 1991 Gulf war, signed a peace treaty with Israel in 1994, and has sought peace with its neighbours. King Abdullah, who succeeded his father in 1999, reafﬁrmed Jordan’s peace treaty with Israel and strong relations

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with the USA. The composite risk rating is less volatile but closely reﬂects the trends in the ﬁnancial and political risk ratings. The four risk ratings in Figure 4.38 reﬂect Kuwait’s small, rich and state-dominated economy. Oil yields almost half of GDP, as well as 90% of export revenues and 75% of government income. The economy beneﬁted from high oil prices in the 1970s, but was affected by the 1982 securities market crash, oil price falls in the mid-1980s, and the 1990– 1991 invasion by Iraq, during which oil exports ceased. After the invasion, the government sought refuge in Saudi Arabia and elsewhere, and relied on its large foreign investments for funding and reconstruction. Production was restored, reﬁneries and facilities were modernized, and oil exports exceeded their pre-invasion levels by 1993. The economic risk rating was virtually ﬂat in the high 80s, apart from 1990 to 1993 and 1998 to 2000, with little volatility but peaks in 1990 and 1999. Iraqi invasion led to a structural change in 1991, when the rating fell to the mid-40s, but rose to the high 80s by 1993. Given the dependence on oil, the rating fell signiﬁcantly in 1998 due to low oil prices, varied in the mid-60s until 1999, but rose by early 2000 as higher oil prices led to a large budget surplus. Generally, the trends and volatility in the ﬁnancial and political risk ratings were virtually the same. Upward trends were observed, apart from 1990 to 1992, when the ﬁnancial and political ratings fell to 17 and 20, respectively, and rose notably by 1992, with virtually no volatility apart from peaks in 1990 and 1991. Kuwait has been ruled by the Sabah family since 1971 and gained independence from Britain in 1961. The 1990 Iraqi invasion led to a military intervention by the US-led UN coalition in 1991 that led to liberation in 4 days. No political parties exist in Kuwait and the National Assembly plays a major role in political decisions. In 1999, the Emir issued major decrees affecting women’s right to vote, economic liberalization, and nationality, but these were rejected by the Assembly. After the invasion, the focus was to develop ties with states that had participated in the UN coalition, which were given a lead role in the country’s reconstruction. Relations with nations that supported Iraq, including Jordan, Sudan and Yemen, remain strained, and Arafat’s support for Iraq during the war has affected Kuwait’s position toward the PLO and the peace process. The trends and volatility in the composite risk rating are virtually the same as those of the ﬁnancial and political risk ratings. The risk ratings, risk returns and volatility in Figure 4.39 are for Lebanon, one of the most complex countries in the Middle East. Lebanon won independence in 1943 and went through a painful civil war from 1975 to 1991. Regional powers, such as Israel, Syria and the Palestine Liberation Organization, used Lebanon as a battleﬁeld for their own conﬂicts. Israel invaded in 1982 and withdrew to a self-declared security zone in 1985.

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Lebanon embarked on a massive program in 1992 to rebuild the devastated physical, economic and social infrastructure. The Lebanese economy is service oriented, with no restrictions on foreign investment and strictly enforced bank secrecy. However, the investment climate suffers from red tape, corruption, arbitrary licensing decisions, high taxes and fees, archaic legislation and a lack of intellectual property protection. Family remittances, banking services, manufactured and farm exports, and international aid are the main sources of foreign exchange. Annual inﬂation fell from 100% to almost 0 during the 1990s, and the economy grew at 8% in 1994, 7% in 1995 and 4% in 1996– 1997, but fell to around 0 from 1998 to 2002. Sustainable growth remains a serious challenge. An austerity program was implemented to reduce government expenditures, increase revenue collection and privatize state enterprises. Overall, economic risk rating fell to 1988, rose to mid-70s by 1992, and fell to low 50s by 2002, with noticeable volatility peaks. Financial, political and composite risk ratings had falling trends to 1991 and rising trends to 2002, with the composite risk rating reﬂecting the volatility in political and ﬁnancial ratings. Lebanon has built a more equitable political system since the end of the war in 1991. There have been several successful elections, militias have weakened and the Lebanese Armed Forces have returned control of much of the country. Hizballah, the radical Shia party, retains its weapons, while Syria has around 16,000 troops in the country. Since Israel’s withdrawal in 2000, Lebanese Christians and Druze have demanded that Syria also withdraw its forces. Peace remains fragile, but a high literacy rate and laissez-faire mercantile tradition make Lebanon an important commercial centre. Figure 4.40 plots the four risk ratings, risk returns and volatility for Libya. Following Colonel Muammar Abu Minyar al-Qadhaﬁ’s military coup in 1969, Libya has adopted a political system which combines socialism and Islam. The government controls the oil resources, which account for about 95% of exports, 75% of government receipts and 30% of GDP. Oil revenues are the principal source of foreign exchange. High oil revenues and a small population give Libya one of the highest per capita GDPs in Africa. However, economic mismanagement has led to high inﬂation and import prices, and a decline in living standards. Agriculture is the second largest economic sector. Despite government efforts to diversify the economy and encourage the private sector, price, credit, trade and foreign exchange controls have imposed signiﬁcant constraints. Import restrictions and inefﬁcient resource allocation have caused periodic shortages in food and basic goods. Economic and ﬁnancial risk ratings had rising trends throughout the sample, but with different patterns. Signiﬁcant progress was made in the early 2000s on economic reforms to integrate

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the country into the international community. The two ratings were dominated by volatility peaks, with little volatility. After September 11, 2001, economic risk rating fell from high to low 80s, and ﬁnancial risk rating fell from high 80s to low 70s in 2002. Qadhaﬁ used oil funds in the 1970s and 1980s to promote his ideology internationally by supporting rebels and terrorists against Marxism and capitalism. In early 1973, he engaged in military operations in northern Chad’s Aozou Strip to gain access to minerals, but was forced to retreat in 1987. Political risk rating had a rising trend to 1993, falling trend to 1999, and rising trend to 2002, with little volatility but a peak in 1991. Libya’s implication in the 1988 bombing of PanAM Flight 103 over Lockerbie, Scotland led to UN sanctions in 1992, and then economic and political isolation. Recently, Libya has undergone a dramatic rehabilitation. Support for terrorism fell after the UN sanctions, and relationships with Europe have been rebuilt. Overall, the composite risk rating reﬂects the trend and volatility in the political risk rating. The four risk ratings, risk returns and volatilities in Figure 4.41 are for Morocco, which is situated along the Atlantic and Mediterranean coastlines. A French protectorate from 1912 to 1956, Morocco is now bidding for EU membership. As a developing country, Morocco faces high government spending, constraints on private activity and foreign trade, and unstable economic growth. Given a strategic location, tourism has become an important sector for economic development, with tourism investment having increased since 1999. The government has pursued economic and ﬁnancial reforms that are supported by the IMF, World Bank and Paris Club, to stimulate growth and reduce high levels of unemployment. Agriculture, which is vulnerable to inconsistent rainfall, is the major source of employment. Despite structural reforms, the dirham is fully convertible for current account transactions only. Large foreign exchange reserves were generated from the sale of a mobile telephone license, and partial privatization of state-owned telecommunications and tobacco companies. Reﬂecting these conditions, the economic risk rating had a generally rising trend to early 1994, followed by no trend and three signiﬁcant falls in 1996, 1997 and 2000. Financial risk rating had a rising trend to 1994, followed by no trend around the mid-70s. In 1997, ﬁnancial risk rating fell by almost 10 points and rebounded to its former level in 1998. In general, economic risk rating exhibited greater volatility and peaks than ﬁnancial risk rating. No trends in either rating remain as major challenges, which include structural adjustments for free trade with EU and USA, improving education, and attracting foreign investment to boost employment and youth living standards. There are virtually identical trends and volatility for political and composite risk ratings. Both ratings

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rose to 1992, after which there was no trend, with the ratings ranging around 70 and having little or no volatility. Morocco’s long ﬁght for independence from France ended in 1956. In the late 1970s, Morocco annexed Western Sahara, and the status of this territory remains unresolved. Gradual political reforms in the 1990s resulted in the establishment of a bicameral legislature in 1997. Figure 4.42 plots the four risk ratings, risk returns and volatility for Oman, the oldest independent state in the Arab world. Oman is a wealthy economy with oil as the main sector, but is a modest oil producer compared with the other Gulf nations. Oil production rose in 1976 but declined gradually to late 1980 due to the depletion of recoverable reserves. For 1981– 1986, Oman compensated for declining oil prices by increasing oil production, but collapsing oil prices in 1986 led to a dramatic fall in revenues. By mid-1987, increased production led to higher revenues, and oil production increased to 2000. The government has proceeded with privatization, liberalization of investment opportunities to attract foreign capital and higher budgetary outlays in health services and education. The sixth 5-year plan, launched in 2000, focused on income diversiﬁcation, job creation for Omanis in the private sector and reduced economic dependence on oil. Like Qatar and the UAE, expatriate workers have been replaced by Omanis to reduce unemployment and limit dependence on foreign labour. Given these developments, economic risk rating had a slight increasing trend, with three major falls in 1986 – 1987, 1991– 1992 and 1998– 1999. There is little volatility but numerous volatility peaks in the sample. Financial risk rating was less volatile than economic risk rating. Overall, the rating was 62 prior to 1990, increased to high 80s by 1992, and remained ﬂat thereafter. Following a coup in 1970, Sultan Qaboos Bin Said replaced his father and opened up Oman to the outside world after a long period of isolation. He used oil revenues to develop infrastructure and modernize the government structure. Consequently, the Consultative Assembly, established in 1981, was replaced by the Consultative Council in 1990 and the Council of State in 1997. Oman’s moderate and independent foreign policy has led to good relations with all Middle Eastern countries. As a result, the political risk rating had a generally rising trend after the establishment of the Consultative Council in 1990, with a clustering of volatility and volatility peaks. In general, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings. The four risk ratings, risk returns and volatility for Qatar are given in Figure 4.43. Following the discovery of large oil and gas resources in the 1940s, previously poor Qatar has emerged as one of the richest countries in the Gulf region. The oil and gas sectors account for around 55% of GDP,

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85% of exports and 70% of government revenues. Proven oil reserves ensure continued output until 2023, while proven natural gas reserves are the world’s third largest. A long-term goal is to develop offshore natural gas reserves to offset the ultimate decline in oil production. Economic risk rating had a rising trend to 1993, falling trend to 1999, and rising trend to 2002, with mild volatility from 1986 to 1991 and peaks in 1994 – 1999. Financial risk rating was 52 until late 1989, rose to high 80s by early 1995, fell to mid-50s in late 1998 and rose to high 70s by May 2002. There was virtually no volatility, apart from peaks in 1991– 1992, 1997 and 1998– 1999. The economy was boosted in 1991 by the completion of the $1.5 billion North Field Phase I gas development. However, after the mid1990s, the economy slowed down due to OPEC quotas on crude oil production, lower oil prices and reduced oil earnings. As a result, government spending plans were reduced to match lower incomes, and many ﬁrms retrenched expatriate staff. With the economy recovering in the late 1990s, the expatriate staff increased, particularly from Egypt and South Asia. Since 2000, high oil prices and increased natural gas exports have led to continued trade surpluses. Qatar has pursued a vigorous program under which all joint venture industries and government departments strive to move Qatari nationals into positions of greater authority. Qatar has been ruled by the Al Thani family since the mid1800s. During the late 1980s to early 1990s, the economy was affected by oil revenue mismanagement by the Amir, who had ruled since 1972, but was overthrown by his son in a bloodless coup in 1995. In 2001, Qatar resolved its border disputes with both Bahrain and Saudi Arabia. Overall, the political risk rating had a rising trend, with volatility concentrated from 1988 to 1997. Overall, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings. The four risk ratings for Saudi Arabia are given in Figure 4.44. Ruled as an absolute monarchy since its creation in 1932, Saudi Arabia emerged from an underdeveloped desert kingdom to be one of the wealthiest Middle Eastern nations due to its vast oil resources. The oil reserves are the world’s largest and account for more than 90% of national exports and almost 75% of government revenues. Since 1970, economic development has been based on 5-year plans focusing on infrastructure, education, health and social services, private enterprise, foreign investment and consolidation of national defence. The 2000 – 2004 plan emphasized economic diversiﬁcation, a greater role for the private sector, and expansion of the oil and gas industry. There are noticeable structural changes in the ﬁnancial and political risk ratings, but not the economic risk rating, which followed a generally increasing trend, with discernable volatility throughout the sample. From 1997 to 1999, low oil prices

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slowed state-led industrial development, causing the rating to fall. In 2000, the rating started to increase, due to high oil prices, but fell again in 2001, due to the sharp fall in oil prices. Recently, the government focus has been on providing sustainable utilities, services and jobs for a growing population. The Gulf Crisis in early 1991 was associated with structural changes in the ﬁnancial and political ratings. Such changes indicate that, since 1991, Saudi Arabia has been safer with respect to ﬁnancial and political risk. Political and judicial institutions are ruled by Islamic principles, with the King holding absolute legislative and executive power. The government faces a number of political challenges, with an overstaffed civil service, conservative education system, and widespread corruption and waste. There are fears that the large unemployed youth could be drawn to radical Islamic groups. Saudi Arabia’s foreign policy objectives are to maintain security and a dominant position in the Arabian Peninsula, defend general Arab and Islamic interests, promote solidarity among Islamic governments and maintain cooperative relations with major oil-producing and oil-consuming nations. The government has acted as a mediator in regional crises and in Israeli – Palestinian peace negotiations. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the ﬁnancial and political risk ratings. Figure 4.45 presents the four risk ratings for Syria, which gained independence from France in 1946. There have been periods of political instability owing to conﬂicts within society. Syria has a diversiﬁed and state-led economy, based on agriculture, industry and energy. State intervention since the 1960s and price, trade and foreign exchange controls have hampered economic growth. Despite reforms and ambitious development projects in the early 1990s, the economy still faces numerous weak public sector ﬁrms, low investment, and low industrial and agricultural production. The economic risk rating was ﬂat until 1990 and followed a generally rising trend to 2002, with discernable volatility and a peak in 1990. In 2001, the government approved the operation of private banks for the ﬁrst time in 40 years, but there was no support for a gradual strengthening of the private sector. External factors, such as the international war on terrorism, the Israeli –Palestinian conﬂict and fall in oil prices have hindered foreign investment, government revenues and economic growth. The ﬁnancial risk rating had no trend until 1990, but had a generally rising trend to 2002, with little associated volatility apart from peaks in 1991 and 1999. Foreign debt to key creditors was eased in the mid-1990s, but debts to the former Soviet Union remain unresolved. A generally rising trend in the political risk rating until 1993 was followed by a falling trend to 2002, with greater volatility before 1992. President Bashar, who was conﬁrmed in 2000 after the death of his father, Haﬁz

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Al’Assad, wields virtually absolute authority. A state of emergency has existed since 1963, with martial law due to the state of war with Israel and continuing threats by terrorist groups. In 1967, Syria lost the Golan Heights to Israel, but the civil war in Lebanon extended its regional inﬂuence. Policy issues include national security, increasing inﬂuence among its Arab neighbours, and achieving an Arab – Israeli peace settlement, including the return of the Golan Heights. Relations with the Arab nations were strained by Syria’s support of Iran in the Iran– Iraq war of 1980– 1988, but a slow reintegration process began after the war. In 1989, Syria supported Egypt’s entry to the Arab League, which marked the end of its opposition to Egypt and Sadat’s initiatives towards Israel in 1977 – 1979. Overall, the composite risk rating closely reﬂects the trends and volatility in the ﬁnancial and political risk ratings. Figure 4.46 plots the risk ratings, risk returns and volatility for Tunisia, situated in the centre of North Africa and close to vital shipping routes. Tunisia has a diverse economy, with important agricultural, mining, energy, tourism and manufacturing sectors. Over the past decade, the government has gradually reduced its control over economic affairs. An overvalued dinar and a growing foreign debt led to a foreign exchange crisis in the mid-1980s. In 1986, the government launched a structural reform program that was praised by international ﬁnancial institutions. In 1992, Tunisia re-entered the international capital markets for the ﬁrst time in 6 years, securing a $10 million line of credit to support the balance of payments. The government has liberalized prices, reduced tariffs, lowered debt-service-to-exports and debt-to-GDP ratios, extended the average maturity of its $10 billion foreign debt, and reoriented Tunisia toward a market economy. Structural reforms led to additional loans from the World Bank and other Western creditors. Growth at an average 5% after mid1990s fell to 1.9% in 2002 because of droughts, slow investment and sluggish tourism. Unemployment is aggravated by a rapidly growing work force, with about 55% of the population under the age of 25. As a result, economic risk rating had a generally upward trend, reaching mid-70s by 2002, with mild volatility, apart from volatility peaks. Financial risk varied in the low 50s prior to 1992, when a structural change occurred due to a $10 million line of credit. After 1992, the rating varied in low 70s, with little volatility, apart from a peak in 1992. After independence from France in 1956, President HabibBourguiba established a strict one-party state and dominated the country for 31 years, enforcing an anti-Islamic line and female emancipation. Bourguiba was dismissed on the basis of senility and replaced by Zine El Abidine Ben Ali in 1987. Tunisia has taken a moderate, non-aligned stance in its foreign relations, and sought to defuse rising pressure for a more open society. Political risk rating had an

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increasing trend over the sample, with mild volatility and peaks in 1987 and 1992. Overall, the composite risk rating closely reﬂects the trend and volatility in the political risk rating. Figure 4.47 plots the risk ratings, risk returns and volatility for the United Arab Emirates (UAE), a federation formed in 1972 by the seven Trucial States after independence from Britain. Prior to the discovery of oil in the 1950s, UAE was dominated by pearl production, ﬁshing, agriculture and herding. Since 1973, UAE has emerged as a modern state with high living standards. Oil has dominated the economy, accounting for much of export earnings and providing signiﬁcant investment opportunities. UAE has an open economy and is sensitive to oil and gas price ﬂuctuations. At present production levels, oil and gas reserves should last for more than 100 years. The government is spending on job creation and infrastructure expansion, and is allowing greater private sector involvement. An important foreign exchange earner, the Abu Dhabi investment authority, manages an estimated $150 billion in overseas investments. More than 200 factories operate at the Jebel Ali complex in Dubai, which includes a deepwater port and a free trade zone for manufacturing and distribution of goods. Except for the free trade zone, the UAE requires at least 51% local citizen ownership in all businesses in order to move the Emirates into leadership positions. Economic risk rating had a generally increasing trend, with a noticeable and consistent clustering of volatility. Unlike the economic risk rating, ﬁnancial and political risk ratings had similar trend patterns, with the latter having lower volatility than the former. The two ratings had rising trends to 1992, and generally ﬂat trends to 2002. By May 2002, economic and ﬁnancial risk ratings had reached the high 80s, and political risk rating the high 70s. Although each state maintains a large degree of autonomy, UAE is governed by the Supreme Council of Rulers, consisting of the seven Amirs, who appoint the prime minister and the cabinet. UAE is one of the most liberal countries in the Gulf region, with other cultures and beliefs generally tolerated. UAE’s generosity with oil revenues and a moderate foreign policy stand have allowed it to play an important role in regional affairs. Overall, the composite risk rating reﬂects the trends in the ﬁnancial and political risk ratings, but is less volatile than the ﬁnancial risk rating. Figure 4.48 presents the risk ratings, risk returns and volatility for the fertile and wealthy country of Yemen, still one of the world’s least developed. The Republic of Yemen was formed in 1990 when the traditionalist North Yemen and the Marxist South Yemen were united after two decades of hostility and border wars. While Yemen maintains its tribal ways, structural and institutional reforms for modern economic management have been accompanied by the introduction of a democratic, multi-

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party political system. However, a high population growth rate and internal political disagreements remain problematic. Further reforms are needed to encourage tourism and to address the water crisis. The economy worsened during the post-uniﬁcation period 1990– 1994. Real GDP fell by almost 50%, while unemployment, inﬂation, demand for basic goods, budget deﬁcits and balance of payments deﬁcits rose substantially. Surprisingly, the economic risk rating fell by 40 and was ﬂat in 1991, after which it had a steep rising trend to 1994. The 1994 southern separatist conﬂict led to a fall in the rating, after which it had a generally increasing trend. Sizeable increases in oil exports in the mid-1990s led to strong growth amid periodic oil price declines. Overall, the economic risk rating exhibits some volatility only for 1991 – 1996, with a volatility peak in 1991. The ﬁnancial rating was very low until 1992, after which it increased substantially, ranging from 50 to 60, apart from a large fall in 2000. Except for two volatility peaks in 1991 and 2000, there is little noticeable volatility for the ﬁnancial risk rating. The IMF-supported structural reforms led to signiﬁcant relief and restructuring of foreign debt. For political risk rating, the generally rising trend to 1997 was followed by a generally falling trend to 2002, with higher volatility prior to 1995. The political risk rating fell in 1992 after uniﬁcation, and in 1994 due to the devastating civil war in the south. Following the attacks on a US warship and a French tanker, and the bomb explosion at the British Embassy in 2000, Yemeni authorities arrested suspected terrorists and acquired equipment and anti-terror training. Overall, the composite risk rating reﬂects the trends and volatility in the three component risk ratings. 4.3.5. North and Central America Figures 4.49– 4.63 present the four risk ratings, risk returns and the associated volatilities for the 15 North and Central American countries, namely Bahamas, Canada, Costa Rica, Cuba, Dominican Republic, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Trinidad and Tobago and the USA. Figure 4.49 presents the four risk ratings, risk returns and their associated volatility for the Bahamas. Comprising 700 islands and islets, the Bahamas attracts more than ﬁve times its population in tourists, the majority coming from the USA. With a high per capita income and a concentration of wealth in the main commercial and tourist centres, the Bahamas has experienced remarkable growth in the services sector, namely tourism and ﬁnancial services. Economic performance has been mixed over the sample period, with the economic risk rating having a slightly declining trend. The economic risk rating fell to 1988, had a rising

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trend to 1997, a sharp decreasing trend to 1999 and a rising trend to 2002. Strong growth in the tourism sector and a boom in the construction of new hotels, resorts and residences led to a solid GDP growth after 1999. However, the slowdown in the US economy and the events of September 11, 2001 hampered economic growth in 2002, resulting in a ﬂat economic risk rating. In general, the volatility associated with the economic risk rating increased substantially after 1995. Surprisingly, the ﬁnancial risk rating was ﬂat for much of the sample period, with little volatility after 1996 and a peak in 1998. Financial risk rose signiﬁcantly after the sharp fall in the risk rating in 1998. The rating remained ﬂat in the low 60s until 2002. With few domestic resources and a small industry, the Bahamas is heavily reliant on imports of food and manufactured goods. The domestic resistance to foreign investment due to local concerns about foreign competition has resulted in stagnant growth in many sectors, which clearly need diversiﬁcation. Prior to 1992, the political risk rating was rather ﬂat in the high 60s, with little or no volatility. Under the leadership of the late Sir Lynden Pindling, the Progressive Liberal Party (PLP), which led the Bahamas to independence in 1973, was dominant until 1992. Political risk fell considerably after 1992 when the Free National Movement (FNM) won the general elections against the PLP, which faced accusations of corruption and drug trafﬁcking. There was greater volatility after 1992, as the rating increased, reaching the mid-80s by 2002. The FNM lost to PLP in 2002 after 10 years in power. Overall, the composite risk rating reﬂects the trends in the three component risk ratings, and is generally less volatile than the economic risk rating. The four risk ratings in Figure 4.50 are for Canada, the second largest country and eighth largest economy in the world, and highly integrated with the USA economy, which absorbs 85% of Canadian exports. US actions that affect Canadian exports have created particular tensions. There is growing concern about environmental pollution from the US factories near the border and the exploitation of oil deposits in Alaska. Until 1990 the economic risk rating was generally ﬂat, after which it followed a decreasing trend due to a recession. By the end of 1992, with the ﬁnalization of NAFTA, the risk rating increased and followed an increasing trend until 2000, with a discernible clustering of volatility. The economic downturn in 2001, in response to the US recession and the terrorist attacks of September 11, 2001, caused the rating to fall, after which it followed an increasing trend. Federal ﬁnances remain strong, federal debt as a percentage of GDP is falling, and the government is strongly committed to medium-term tax cuts and higher expenditure. Though there was little variation in the ﬁnancial risk rating before 1997, a structural change in 1997 led to a reduction of almost 20 points, after which

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there was some variation but no trend. There was, in general, little volatility in the ﬁnancial rating for Canada, especially before 1997. Until 1993, the political risk rating ﬂuctuated, a pattern which was repeated with associated clustering of volatility. The rating followed an increasing trend after the 1993 elections, which saw Jean Chretien elected prime minister when the Conservatives were defeated by the Liberals. Re-election of the Liberal Party in 1997 led to an increase in the political rating. The events of September 11, 2001 had a negative impact on the rating, which is associated with a peak in volatility. Canada’s foreign policy differs from that of the USA. The government has refused to support the USA trade embargo on Cuba, and has supported a worldwide ban on the production, export and use of anti-personnel landmines. Recently, the relationship with the USA has been soured by security and trade disputes. Prior to 1993, the composite risk rating for Canada increased and then decreased, after which it had a slightly increasing trend. There was greater apparent volatility in the composite risk rating than in the three component risk ratings. The four risk ratings, risk returns and volatility for Costa Rica are given in Figure 4.51. Following two decades of successful economic diversiﬁcation, the economy has improved substantially, becoming larger than most of its neighbours. Major economic resources are fertile land, frequent rainfall and geographical location in the Central American isthmus, with easy access to North and South American markets, and direct ocean access to Europe and Asia. The construction of a microprocessor manufacturing plant in 1998 had a signiﬁcant economic impact. Economic growth depends on tourism, agriculture and hi-tech electronic exports. A combination of political and economic stability, and a well-educated workforce, has encouraged substantial foreign investment inﬂows, especially from the USA. However, the government is still struggling with a large deﬁcit and internal debt. High inﬂation remains a problem due to increasing import prices, labour market rigidities and ﬁscal deﬁcits. Economic potential should improve signiﬁcantly with the proposed Free Trade Agreement and an EU – Central America co-operation agreement. Economic risk rating increased by almost 30, reaching the low 70s in 1990, had no trend to 2002, and had higher volatility prior to 1990. There was a structural change in the ﬁnancial risk rating from 1990 to 1992, which was virtually ﬂat in the high 40s prior to 1990 and was in the mid-70s after 1992, with noticeable volatility clustering, especially from 1990 to 1992. Costa Rica has been relatively stable and crime free, with only two brief periods of violence since the late 19th century. Strong emphasis has been placed on the development of democracy and respect for human rights. Political risk rating had a generally increasing trend until 2001, after which it fell by almost eight. With no armed forces, Costa Rica has avoided

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political interference by the military, contrary to the experience of its Central American neighbours. However, Costa Rica is increasingly used as a transit point for South American drugs, and it has been alleged that drug trafﬁcking has ﬁnanced the two major political parties. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. The risk ratings for Cuba are presented in Figure 4.52. Cuba has been a Communist country since Fidel Castro led his army to victory in 1959. During the Cold War, Cuba relied on strong Soviet support, and built reputable health and education systems. Amid the USA trade sanctions, Castro failed to diversify the economy, which continues to depend on sugar exports. Economic hardship was heightened by the high price of foreign ﬁnancing. Cuba relies heavily on short-term loans to ﬁnance imports. The government defaulted on most of its international debt in 1986 and does not have access to credit from international ﬁnancial institutions, such as the World Bank. There was a falling trend in the economic, ﬁnancial and political risk ratings until 1992, after which the economic and ﬁnancial ratings rose, but the political rating increased and fell before increasing again in the last 2 years. The falling economic and ﬁnancial ratings were due to the withdrawal of aid from the former Soviet Union, as well as domestic incompetence, which led to a severe economic recession in 1990– 1992. In response to the economic crisis, in 1993 and 1994 the government launched some market reforms, including opening tourism, allowing foreign investment from Canada, Europe and Latin America, legalizing the dollar, and authorizing self-employment for 150 occupations, which resulted in modest economic growth. However, living standards by 2000 have remained well below 1989 levels, as lower sugar and nickel prices, higher petroleum costs, a post-September 11, 2001 decline in tourism, and a devastating November 2001 hurricane, created new economic pressures. Furthermore, the legalization of the US dollar created a serious economic gap between those with and without access to dollars. Continuing hardships led to an increase in prostitution, corruption, black market activity and desperate efforts to escape the country. After the Cold War, Cuba abandoned monetary support for guerrilla movements in Latin America and Africa, but maintained relations with several guerrilla and terrorist groups. The USA Helms-Burton legislation against trade with, and investment in, Cuba in 1996 led to a decreasing political risk rating. There is a discernable clustering of volatility in the three component risk ratings, with the composite risk rating reﬂecting the trends and volatility in the economic and ﬁnancial risk ratings. The four risk ratings, risk returns and their associated volatility for the Dominican Republic are given in Figure 4.53. Sharing the Island of

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Hispaniola with Haiti, the country has become the most popular tourist destination in the Caribbean, with tourism and free-trade zones as key sources of foreign exchange. While still one of the poorest countries in the Caribbean, with a large wealth gap between the rich and poor, the Dominican economy has experienced a very fast growth rate over the past decade. As a result, the economic risk rating had a rising trend over the sample period, with higher associated volatility prior to 1992 and a peak in 1985. The rating reached a level of 80 in 1997, after which it followed a slightly declining trend. Unlike the Bahamas, the ﬁnancial risk rating for the Dominican Republic had a generally rising trend throughout the sample period, with little associated volatility, apart from a peak in 1990 and few extreme observations. The rating rose by almost 10 points in 1997, after which it remained ﬂat in the low-mid 70s. Large tourism earnings and remittances have helped to build foreign exchange reserves. However, the government faces several economic policy challenges, given the widening merchandize trade deﬁcit, large foreign debt payment arrears, high real interest rates, low tax collection and lower demand for exports due to the world economic slowdown. The political risk rating was below the mid50s until early 1993, after which the rating increased steadily until 1998 and followed a generally decreasing trend until 2002. From 1993, there was a discernable increase in the associated volatility, with a number of peaks and extreme observations. Joaquin Balaguer, elected president in 1966 and leader of the Christian Social Reform Party, dominated politics for much of the following 30 years. He was re-elected in 1994, but agreed to serve only a 2-year term after being accused of fraud. Since 1996, regular competitive elections have been held, in which opposition candidates have won the presidency. A serious concern for the Dominican Republic remains the growing immigration from, and political instability in, Haiti. In response, the government has re-evaluated its relations with its neighbour at both the country and international levels. In general, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.54 plots the risk ratings, risk returns and volatility for El Salvador, the most densely populated country in the American mainland and the most industrialized economy in Central America. During the 1980s, El Salvador experienced a ravaging civil war, which ended in 1992. The war was due to a large income inequality between the small wealthy elite, who dominated the government and economy, and the rest of the population, which lived in abject poverty. Aggressive economic reforms and political stability after the 1992 peace accords led to improved investor conﬁdence and greater private investment. Such reforms included the privatization of the banking system, telecommunications, public pensions,

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electrical distribution and electrical generation, a reduction in import duties and elimination of price controls. Greater inﬂows of family remittances and moderate post-war foreign reconstruction aid also helped the economy. Following the collapse of world coffee prices, which caused substantial reductions in coffee production and rural employment, El Salvador used ﬁscal incentives for free trade zones, of which there are presently 15, to create new export industries. Although external and domestic problems, such as natural disasters, slowed down economic growth from 1996 to 1999, the country enjoyed the lowest inﬂation rate in Central America due to prudent monetary policy. In 2001, the US dollar was adopted as the domestic currency. Economic risk rating rose to 1995 and fell slightly to 2002, while ﬁnancial risk rating was ﬂat in the mid-30s until the 1992 peace agreement, after which it rose reaching the mid-80s in 2002. There is higher volatility in economic risk rating prior to 1994, and little volatility in ﬁnancial risk rating, with volatility peaks throughout the sample. As expected, political risk rating rose signiﬁcantly after the 1992 peace agreement between the government and the leftist rebels, which provided for military and political reforms. Political rating fell slightly after 1998, with some volatility and scattered volatility peaks. Despite the reforms, El Salvador remains one of the most violent and crime-ridden nations in the Americas. Overall, the composite risk rating reﬂects the trends and volatility in the three component risk ratings. Figure 4.55 shows the four risk ratings, risk returns and volatility for Guatemala, the largest and most populous country in Central America, with the agricultural sector accounting for 25% of GDP, 75% of exports and 50% of the labour force. Guatemala experienced a variety of military and civilian governments and a civil war for 36 years, which resulted in more than 100,000 casualties and 1 million refugees. The signing of the peace accord in December 1996 removed a major obstacle to foreign investment, but widespread political violence and corruption continued to dampen investor conﬁdence. Moreover, the ﬁnancial crisis in 1998 disrupted the course of economic progress, while the subsequent collapse of world coffee prices had severe impacts on exports and rural incomes. The economy is dominated by the private sector, which generates about 85% of GDP. There is an unequal distribution of income, with about 75% of the population below the poverty line. Illiteracy, infant mortality and malnutrition are among the highest in the region, while life expectancy is among the lowest in the region. Serious problems include increasing government revenues, aid negotiations with international donors, reforming government and private ﬁnancial operations, controlling drug trafﬁcking and narrowing the trade deﬁcit. In view of these developments, economic risk rating had a falling trend to 1987, rising trend to 1995 and no

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trend to 2002, with a higher volatility prior to 1990. Financial risk rating rose by almost 40 over the sample. There was a signiﬁcant increase in the rating in 1997, following the 1996 peace agreement, with virtually no volatility, apart from peaks in 1997 and 1999– 2000. Political risk rating had an increasing trend to 1997, shortly after the signing of the peace accord, followed by a decreasing trend to 2002, with discernable and scattered volatility clustering. The rating fell to the low 60s by 2002, amid the failure to prosecute perpetrators of violence against labour leaders and the non-reinstatement of illegally ﬁred workers. Overall, Guatemala remains one of the most violent countries in the region. In general, the composite risk rating follows the trend in the political risk rating and is more volatile than the economic and ﬁnancial risk ratings. Figure 4.56 presents the risk ratings, risk returns and their associated volatility for Haiti, one of the poorest countries in the Americas. Haiti has been plagued by political instability, violence, poverty, unemployment, migration and environmental degradation for much of its history since independence in 1804. Especially since the 1980s, these factors have led to economic stagnation and damaged Haiti’s image as a major tourist destination. The country’s infrastructure has deteriorated, and drug trafﬁcking has corrupted both the judicial system and police force. There was a slight upward trend in the economic risk rating over the sample period, with discernable clusterings of volatility and volatility peaks. A military coup in 1991 led to a sharp fall in the economic risk rating, which rose in 1992. However, after 1999, the rating fell consistently, reaching the mid-50s by 2002. Following serious irregularities in the 2000 legislative elections, international donors such as the USA and EU suspended almost all aid to Haiti, leading to a signiﬁcant contraction in the economy. Surprisingly, despite inadequate economic and ﬁnancial reforms and slow economic progress after 1991, the ﬁnancial risk rating had a rising trend after 1990, reaching the low 70s in 2002. There was a noticeable associated volatility throughout the sample period. As with the ﬁnancial risk rating, the political risk rating had an increasing trend after 1990, with discernable volatility after 1987 and peaks in 1987, 1993 and 1994. The risk rating fell to the high 40s after 2001, reﬂecting a high associated political risk. More than three decades of dictatorship, followed by a military regime, ended in 1990 when Jean-Bertrand Aristide was elected president. Aristide was ousted in a 1991 military coup, which triggered sanctions by the USA and the Organization of American States. He returned to ofﬁce in 1994, after the military regime had relinquished power and US troops landed peacefully to oversee the transition to civilian government. Aristide won a second term as president in 2000, and took ofﬁce early in 2001. However, a political crisis stemming from fraudulent legislative elections in 2000

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remained unresolved in 2002. In general, the composite risk rating reﬂects the trends and volatility in the ﬁnancial and political risk ratings. The four risk ratings, risk returns and volatility for Honduras are plotted in Figure 4.57. Military rule, corruption, extreme income disparity, high unemployment, crime and natural disasters have left Honduras as one of the least developed and least secure countries in Central America. Industrial development has been limited and the economy depends on exports of coffee and bananas. After 25 years of military rule, a civilian government was freely elected in 1982. Over the last decade, the economy has become diversiﬁed, new export sectors have been developed and the maquila industry has been established. Currently, Honduras relies strongly on expanded trade privileges under the Enhanced Caribbean Basin Initiative and on debt relief under the Heavily Indebted Poor Countries Initiative. Investment incentives to attract foreign capital in export industries have been introduced. While the country has met most of its macroeconomic targets, it has failed to meet IMF targets to liberalize the energy and telecommunications sectors. As the USA is the major trading partner, the economy depends heavily on domestic American economic performance. Crucial economic factors include low commodity prices and a reduction in the high crime rate. Honduras was devastated by Hurricane Mitch in 1998, which killed almost 5600 people, destroyed 70% of crops, and led to a damage bill of around $2 billion. There is a slightly increasing trend in economic risk rating, with two signiﬁcant falls in 1989– 1990 and 1998. The rating reached 64 in 2002, and was associated with little volatility throughout the sample and a volatility peak in 1998. Unlike the economic risk rating, ﬁnancial risk rating increased steadily after 1988, reaching the low 70s in 2002. Although low, the ﬁnancial rating has increased in spite of the unstable political climate, which affects investor conﬁdence. Similarly, political risk rating followed an increasing trend from the high 30s in 1984 to mid-60s in 2002, with noticeable and scattered volatility clustering. The political climate improved substantially after the end of military rule, but has remained sensitive to crime and corruption. Overall, the composite risk rating reﬂects the trend and volatility in the political risk rating. The four risk ratings, risk returns and associated volatility for Jamaica are given in Figure 4.58. Although a politically stable country, Jamaica does not enjoy social and economic harmony, with both luxurious tourist resorts and densely populated and impoverished ghettos. Deteriorating economic conditions during the 1970s led to recurrent violence and a fall in tourism. The Jamaican economy depends heavily on tourism and bauxite, and has the potential for growth and modernization despite widespread crime and poverty. There was a general rising trend in the economic risk

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rating, with discernable volatility and peaks throughout the sample period. The rating fell considerably during 1988– 1992 and 1996 – 2000, after which it rose by almost 10 points and remained low in the mid-60s. After 5 years of recession, the economic performance improved in 2000, but the global economic slowdown and the events of September 11, 2001 hampered the economic recovery. The ﬁnancial risk rating had a generally rising trend to 1996, after which the rating fell by more than 10 points and remained ﬂat in the mid-70s. There was a discernable clustering of volatility and a peak in 1997. High interest rates, increased foreign competition, an unstable exchange rate and an increasing merchandize trade deﬁcit still remain unresolved. Government bailouts to various ailing sectors of the economy, particularly the ﬁnancial sector, have resulted in a growing internal debt. Economic progress depends upon encouraging investment and tourism, maintaining a competitive exchange rate, selling off reacquired ﬁrms, and implementing proper ﬁscal and monetary policies. Jamaica gained full independence within the British Commonwealth in 1962. The political risk rating fell until 1986, rose to the low 80s in 1998, and fell to the high 60s in 2002, with a noticeable clustering of volatility and an associated peak in 1997. Since the 1980s, subsequent governments have supported open market reforms, but political violence marred elections during the 1990s. The government has occasionally deployed army units to suppress violent crime, with more than 1000 murders reported in 2002. Moreover, there have been accusations of extrajudicial killings by law enforcers. Overall, the composite risk rating closely reﬂects the trends and volatility associated with the ﬁnancial and political risk ratings. The four risk ratings in Figure 4.59 are for Mexico, which two decades ago was closed to foreign investment and trade, with strong government participation. Now one of the world’s most trade-dependent countries, Mexico has Free Trade Agreements with the USA, Canada, EU, and others. There is a large oil sector, which provides a third of government revenues, but is not enough for economic prosperity. However, the country is undergoing substantial change, as the 1997 elections resulted in a victory for the combined opposition, breaking the one-party system with a democratic fac¸ade. The 2000 presidential elections conﬁrmed the development, as an opposition candidate, Vicente Fox, became president for the ﬁrst time. Massive external debt default in 1982, the 1984 oil price crisis, and accession to GATT in 1986, are reﬂected in movements in the economic, ﬁnancial and political risk ratings, which followed a declining trend to 1986. The upward trend after 1986 was due to economic reforms by the government, including trade and investment liberalization, privatization, deregulation and ﬁscal consolidation. From 1988 to 1994,

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President Salinas began a process of restructuring the economy, which was continued by the Zedillo administration from 1994 to 2000. These reforms and growing ties with the USA led to a period of relatively strong growth and stability in the economy. The 1994 – 1995 peso crisis led to a fall in the ﬁnancial rating, which rose and fell in 1997. However, the economy recorded a contraction in 2001, which affected the economic and political ratings, but not the ﬁnancial rating. While the Argentine debt crisis had no signiﬁcant effect on Mexico, the downturn was attributed to economic factors in the USA and the events of September 11, 2001 that led to caution towards US border trade, in particular, and a signiﬁcant reduction in tourism. The socio-political conﬂict in the southern province of Chiapas remains unresolved. In response to pressures for greater rights for indigenous people, President Fox has shown a willingness to deal with the demands of guerrillas. Traditionally, Mexico’s foreign policy has been based on non-intervention and self-determination. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.60 plots the risk ratings, risk returns and volatility for Nicaragua, one of the poorest countries in the Western Hemisphere, which is still struggling to overcome the consequences of dictatorship and civil conﬂicts. Traditionally, the economy has relied on agricultural exports. National wealth was controlled by a few elite families, primarily the Somoza family, which ruled the country from 1937 to 1979. Violent opposition against the government led to a short civil war, bringing the Marxist Sandinista guerrillas to power in 1979. Signiﬁcant progress was made in redistributing property, and in health and education. The Sandinistas won again in 1984, but their Marxist orientation led to a USsponsored anti-Sandinista revolution through the late 1980s. The USA also imposed trade sanctions and mined Nicaraguan harbours. By 1990, when the Sandinistas were defeated, per capita income had fallen by 33.5% from its 1980s level, infrastructure was destroyed and the modest tourism industry had collapsed. Following the restoration of democracy in 1991, economic growth increased, with inﬂation falling from 13,500 to 5.3%. Recent priorities include IMF-supported economic reforms, relief from massive debt burden, encouragement of foreign investment and increased international development aid. Natural disasters after 1996, low world commodity prices and a series of bank failures have adversely affected the economy. Nicaragua is a risky country. Economic risk rating ranged from a low 10 in 1987 – 1988 to 60 in 2002, with rising trend after the restoration of peace, falling trend to 1999, and slight upward trend to 2002. Volatility was higher prior to early 1989, with three peaks in 1987– 1989. There were structural changes in ﬁnancial risk rating in 1990 – 1991 and 1997, which

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ranged between mid-20s and mid-30s prior to 1990, was virtually ﬂat in the high 50s to 1997, and between mid-30s and mid-40s to 2002. There is virtually no volatility, apart from three peaks in 1997– 1999. Political risk rating had a rising trend over the sample, with noticeable volatility throughout the sample, as signiﬁcant progress was made towards consolidating democracy after 1991. Overall, the composite risk rating reﬂects the trends and volatility in the political and economic risk ratings. Located between North and South America, and the Atlantic and Paciﬁc Oceans, Panama is strategically important. The four risk ratings, risk returns and volatility are given in Figure 4.61. The Panama Canal was built by the US Army Corps of Engineers from 1904 to 1914 and generates about 10% of GDP. This made Panama a target of intervention by the USA, which deposed the regime of General Noriega, a former ally, in 1989, and controlled the Panama Canal until 1999. However, the Canal has become less important as international banking, manufacturing, shipping and tourism now provide more jobs and tax revenues. The economy beneﬁts from the Colon free trade zone, the world’s second largest, and the services sector accounts for around 75% of GDP. However, the recent slump in the Colon free zone and agricultural exports due to the world’s economic slowdown led to economic contraction after 2000. Unemployment remains high, while the government has supported public works programs, tax reforms, new regional trade agreements and tourism development to stimulate growth. Although Panama has the highest per capita GDP in Central America, 40% of the population lives below the poverty line. As a result, the economic risk rating was highly volatile, and always higher than 68, but with no general trend. The rating fell signiﬁcantly after the 2000 economic contraction. Unlike the economic risk rating, ﬁnancial risk rating exhibits little volatility, apart from peaks in 1984, 1986, 1990 and 1997. The rating rose after the 1989 collapse of Noriega’s regime, reaching the mid-70s by 1997. There was a fall in 1996, after which the rating had a slightly rising trend to 2002. Panama rebuilt its civilian government after the 1989 annulment of the Noriega regime. As a result, political risk rating had a rising trend after 1989, rose by 20 in 1998, and was ﬂat in the mid70s until 2002. There is little volatility over the sample, apart from peaks in 1994 and 1997, and a clustering in 1989 – 1990. A major challenge is for Panama to repair its reputation as a major trafﬁc point for drugs and illegal immigrants, and a haven for money laundering. Overall, the composite risk rating reﬂects the trends and volatility in the political and ﬁnancial risk ratings. The four risk ratings, risk returns and volatility in Figure 4.62 are for Trinidad and Tobago. This country of two islands is one of the wealthiest in the Caribbean, due to large oil and natural gas revenues. Although not as

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important as in other Caribbean islands, tourism is concentrated mostly in Tobago, and is an increasingly important industry. Given the heavy reliance on oil and natural gas production, the national economic performance suffered signiﬁcantly from falls in world oil prices in the 1980s and 1990s. Thus, the economic risk rating followed decreasing trends from 1984 to 1989, 1991 to 1994 and 1998 to 1999. These periods were characterized by high foreign debt, unemployment and labour unrest. The economic rating increased after early 1999 due to the booming natural gas sector. Overall, there is noticeable volatility in the economic risk rating, especially prior to 1994. Unlike the economic risk rating, the ﬁnancial risk rating followed a rising trend, with little or no volatility, apart from two peaks in 1988 and 1990. Trinidad and Tobago is becoming attractive for international investment. Sound economic and ﬁnancial reforms have resulted in an improved investment climate, amid further diversiﬁcation in tourism, manufacturing and agriculture. The debt service ratio has fallen substantially since 1997, while unemployment has gradually decreased. Trinidad and Tobago joined the British Commonwealth as an independent country in 1962, and adopted a republican constitution in 1976. Domestic unrest has dominated since 1975. The political risk rating had a generally rising trend to 1998, but fell signiﬁcantly, reaching the mid-60s in 2002, with noticeable volatility peaks. As with other countries in the region, drug-related crime and police corruption pose serious threats to the economic, ﬁnancial and political stability of the country. In response, capital punishment was restored in 1999, despite strong international pressure. Moreover, the 2001 general elections led to an unprecedented tie between the governing party and the main opposition, with the prime minister requesting the suspension of parliament due to the tied elections in 2002. Overall, the composite risk rating closely reﬂects the trends and volatility in the three component risk ratings. Figure 4.63 presents the four risk ratings for the USA, the world’s largest economic and military power. The USA was born in a revolution that led to a separation from the British Crown. Drafted in 1787, the constitution established a federal system with a division of powers, even at the central level, and has remained unchanged since its inception. There was a generally increasing trend in the economic risk rating, with a single sharp decrease in 1996 and a clear peak in the associated volatility. The strong economic performance from 1994 to 2000 ended in 2001, with the rating starting a declining trend. Economic imbalances that built up during the preceding boom years, including a low propensity to save and large current account deﬁcit, undermined economic stability. Inﬂationary pressures were controlled by structural and cyclical factors. Only a slight

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negative impact on the rating was discernible from the terrorist attacks of September 11, 2001. Emergency measures to boost the economy and improve domestic security following the terrorist attacks pushed the federal budget into deﬁcit. However, a moderate increase in the rating occurred in late 2001 due to a slight improvement in the trade balance, after which the rating remained ﬂat. There was virtually no change in the ﬁnancial rating until a structural change in 1997, so that volatility was entirely ﬂat before 1997, but mild thereafter. For the political risk rating, a downward trend until 1992 was followed by an upward trend until 2000, and volatility is observed to be tri-modal. The election of the Democratic Party candidate, Bill Clinton, as President in 1992 caused a change in the direction of the political risk rating trend. Perhaps coincidentally, the upward trend ended with the 2000 elections. After a series of legal challenges in January 2001, George W. Bush was elected President, thereby causing the political rating to rise. With the events of September 11, 2001 and their aftermath, the rating fell and remained ﬂat until the end of the sample. Overall, there was a downward trend in the composite risk rating, with greater volatility at the end of the sample. Surprisingly, the tragedy of September 11, 2001 seems to have had only a small impact on the economic risk rating, no apparent impact on the ﬁnancial risk rating, but substantial impacts on both the political and composite risk ratings. 4.3.6. South America Figures 4.64– 4.73 present the four risk ratings, risk returns and the associated volatilities for the 10 South American countries, namely Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay and Venezuela. The four risk ratings for Argentina are given in Figure 4.64. Argentina is rich in resources and has a well-educated workforce, but economic growth has generally not matched expectations. From 1880 to the 1930s, Argentina was one of the world’s 10 wealthiest countries owing to the rapid expansion of agriculture and foreign investment in infrastructure. However, over the past 25 years, Argentina has struggled with military dictatorship, the war over the Falkland Islands, and severe economic difﬁculties. There is a similar pattern, with a discernable clustering of volatility, in the economic and political risk ratings, starting at very low values and following a generally increasing trend until 1999, after which they have returned to their original values. Similarly, the ﬁnancial risk rating increased to 1995 and then decreased, with an associated clustering of volatility. The low ratings in the 1980s were the result of protectionist and populist economic policies in the post-war era that led to economic

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stagnation and hyperinﬂation. When Carlos Menem was elected President in 1989, he abandoned the former policies in favour of market economics and liberalization, resulting in a period of rapid growth. His failure to sustain the ﬁscal and structural reforms in his second term from 1995 to 1999 left the economy vulnerable to the 1994 Mexican ‘Tequila’ crisis, the 1997 South-East Asian crisis, the Russian default of 1998 and the Brazilian devaluation of 1999. These shocks led to a higher cost of foreign borrowing and less competitive exports. An IMF bailout package of nearly $40 billion in late 2000, involving tax rises and cuts in social welfare programs, resulted in a political crisis that caused the government to collapse amid violent protests. A new government was elected in January 2002, when Argentina abandoned the quasi-currency board system, which pegged the peso at parity to the US dollar for over 10 years. The subsequently ﬂoated peso increased a sense of expropriation for depositors, so that almost all dollar loans were converted to the peso at parity. Consequently, bank balance sheets and reputations were destroyed, and the number of banks and the scale of banking operations shrank signiﬁcantly. The banking system, formerly one of the strongest in Latin America, has been decimated. Overall, the composite risk rating closely reﬂects the trends and volatility in the economic and political risk ratings. The four risk ratings, risk returns and volatility in Figure 4.65 are for Bolivia, the highest and most isolated country in South America, and the world’s largest producer of tin. Bolivia is very poor, with wealthy elites of Spanish ancestry dominating the political and economic landscape. The remainder consists of low-income subsistence farmers, miners and artisans. After democracy was established in 1982, Bolivia underwent major reforms to maintain price stability and sustained growth, and alleviate poverty. A gradual opening of its markets and privatization of the major state monopolies in the hydrocarbons, electricity, telecommunications and mining sectors have been important achievements, and led to signiﬁcant foreign direct investment during 1996 –2002. Bolivia’s trade with neighbouring countries has also grown, partly through regional preferential trade agreements. Consequently, the economy has shown steady growth, apart from the recession in 1998– 1999 due to falls in international demand, low commodity prices and internal policies, such as coca eradication and new controls on contraband. However, the economy suffered from 2000 to 2002 due to major civil disturbances, global slowdown and slow domestic activity. Economic risk rating rose by about 45 points to 1996 and fell to the mid-60s by 2002, with higher volatility prior to 1990. After a period with no trend, ﬁnancial risk rating rose by 60 points from 1987 to 1991, and was ﬂat at 70, with virtually no volatility after 1987. Most foreign debt payments have been rescheduled several

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times since 1987 through the Paris Club mechanism. Named after independence ﬁghter Simon Bolivar, Bolivia has experienced nearly 200 coups and counter-coups since independence from Spain in 1825. Democratic civilian rule was established in 1982, but high poverty, social unrest and drug production remain major problems. Political risk rating rose to 1997 and remained ﬂat thereafter, with discernable volatility. Serious issues include attracting foreign investment, improving education, resolving disputes with coca growers and war on corruption. Overall, the composite risk rating closely reﬂects the trend in political risk rating, and is less volatile than the economic and political risk ratings. The four risk ratings for Brazil are given in Figure 4.66. Very rich in natural resources, its economy is based on import-substituting industrialization. The external position declined in the late 1970s, leading to hyperinﬂation, a collapse in investment and deterioration in income distribution. In 1982, Brazil defaulted on payment of one of the world’s largest foreign debts. Consequently, the economic risk rating was volatile to 1989, and varied in the low 40s. The volatile rating followed a generally increasing trend from 1990, reaching the low 70s by 2002. Under the radical economic reforms of 1990, rapid trade liberalization increased import penetration, though imports remained low, indicating a relatively closed economy. Despite the reforms, foreign debt payments were suspended and inﬂation reached a level of nearly 5000% in 1993. Economic risk fell after July 1994 as Brazil embarked on an economic stabilization program, the Plano Real. The rating rose, with one volatility peak in 1995, as the economy recovered and inﬂation fell to 2.5% by 1998. However, the rating fell again in 1998 due to high interest rates and the South-East Asian crisis, but followed an upward trend after 1999. Financial risk was high in 1984 after Brazil defaulted on its foreign debt payment. The rating surprisingly followed an upward trend, reaching a level of almost 80 by 1992, even though the country experienced deep economic and ﬁnancial crises. From 1992 to 1997, the rating had no trend and varied around 70, with little or no volatility. Subsequently, the rating followed a slight decreasing trend with high volatility when the Real was unstable due to shocks, including a domestic energy crisis and the Argentine economic crisis. The political risk rating was low throughout the sample, with no trend but noticeable volatility. Brazil completed the transition from a military regime to an elected government in 1989. Brazilian politics are notable for the fragmented nature of parties and government efforts to form and maintain workable coalitions. Social issues, including indigenous rights and land claims, and the development of the Amazon and the North East, remain unresolved. The composite risk rating had an upward trend

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through to 1998, followed by a downward trend, and was less volatile than the other three risk ratings. Figure 4.67 presents the risk ratings for Chile, one of the world’s most open economies. Reforms such as privatization, liberalization and deregulation of trade and investment were initiated by the military government and continued by subsequent democratic administrations. The economic, ﬁnancial and political risk ratings were low and ﬂat until 1987, after which they followed an increasing trend and then decreased, with virtually no change in the ﬁnancial rating between 1991 and 1997. There are discernable clusterings of volatility for the three component risk ratings. The economic risk rating fell in 1998 and remained low in 1999 as a result of the recession due to the global downturn. However, it increased as economic recovery began in 2000, but followed a slight declining trend to 2002, while both domestic and foreign investments fell, unemployment rose and economic growth slowed. In early 2002, the government committed to undertake microeconomic reforms to create new incentives for private investment. After a period of stability, the ﬁnancial risk rating also fell in 1997 and varied around the mid-70s until 2002. The government implemented further liberalization of capital markets in 2001, leading to a rise in the rating until the end of the sample, and was repaying foreign debt by 2002, which was low by Latin American standards. Changes in the political scene in December 1988, when Augusto Pinochet failed to win a referendum, led to democratic elections in December 1989. There have since been three consecutive presidents from the Concertacion de Partidos por la Democracia coalition. After the defeat of Pinochet’s military regime, the constitution was amended to ease provisions for future amendments and diminish the role of the National Security Council by equalizing the number of civilian and military members. However, further constitutional reforms are still necessary to complete the full transition to democracy. Since the return to democracy, Chile has become an active participant in the international political arena, but the political risk rating decreased in 1998 before increasing in 2000 due to the detention of Pinochet in the UK in response to an extradition request by Spain. This period witnessed demonstrations by supporters and opponents of Pinochet, which led to clashes with the police. Overall, the composite risk rating reﬂects the trends and volatility in the three component risk ratings. Risk ratings, risk returns and volatility in Figure 4.68 are for Colombia, strategically located and badly affected by extended violent conﬂicts. Colombia has substantial oil reserves and is a major producer of gold, silver, emeralds, platinum and coal. The economy is diverse and relatively advanced. After rising to 1987, economic risk rating fell to 1990, but

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increased in 1990 as liberal economic reforms were initiated, leading to tariff reductions, ﬁnancial deregulation, privatization of state-owned enterprises and liberalized exchange rate. Almost all sectors were opened to foreign investment, although agricultural products were protected. Economic rating had a falling trend from 1993 to 1999 due to lower oil production and depressed coffee harvests and prices, followed by a rising trend to 2002, with substantial volatility in 1984– 1988 and 1996– 2002. A contraction in output of 4.3% in 1999 was followed by increases in three subsequent years. In addition to domestic goals of keeping inﬂation low, reducing high unemployment, reforming the pension system, investing in infrastructure and maintaining a stable currency, there was heavy emphasis on trade liberalization. After a period of no trend, ﬁnancial risk rating rose by almost 30 points from 1990 to 1992, fell to high 50s by 1997 and rose to high 70s by 2002, with higher volatility after 1996. Strong ﬁscal policy helped raise new loans from the Inter-American Development Bank and the World Bank. Despite internal security problems, both economic and ﬁnancial risks have gradually improved. A 40-year campaign to overthrow the government escalated during the 1990s. People in the countryside were under the inﬂuence of left-wing guerrillas, whose agenda was now dominated by the lucrative drug and kidnapping trades. In response, the paramilitaries have grown in recent years, challenging the rebels for control of territory and the drug trade. Alongside political violence, drugrelated crimes, such as murder and kidnapping, remain serious problems. As a result, political risk rating fell from high 60s in 1984 to high 40s in 2002, with volatility concentrated from 1997 to 2001. In general, the composite risk rating reﬂects the trends and volatility in the ﬁnancial and political risk ratings. Figure 4.69 presents the four risk ratings, risk returns and volatility for Ecuador, which emerged with Colombia and Venezuela from the collapse of Gran Colombia in 1830. The economy is based on oil production, which accounts for 30% of public sector revenues and 40% of export earnings. Ecuador is the world’s largest exporter of bananas and a major exporter of shrimp, so that ﬂuctuations in world market prices have major domestic impacts. The economic risk rating rose to 1996 and fell to 2000 due to the deteriorating economy in 1997– 1998 and the severe economic and ﬁnancial crisis in 1999. The crisis was triggered by a number of external shocks, including natural disasters in 1997, declines in global oil prices in 1997– 1998 and international emerging market instability in 1997 – 1998. These factors highlighted the unsustainable economic policy mix of large ﬁscal deﬁcits and expansionary money policy, and resulted in contraction of GDP by 7.3%, hyperinﬂation, and a 65% devaluation of the currency in 1999. The banking system also collapsed, and Ecuador defaulted on its

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external debt, so that ﬁnancial risk rating rose to the low 70s by 1993, but fell by almost 35 by 2000. Economic and ﬁnancial risk ratings rose after 2000 due to the 2001 adoption of the US dollar as the domestic currency, which stabilized the economy. Growth returned to its pre-crisis levels, but slowed down in 2002. In general, economic risk rating is more volatile than ﬁnancial risk rating. In 1992, Duran-Ballen won the presidency for the third time and succeeded in pushing a limited number of economic reforms through Congress. The vice-president, the architect of the economic policies, ﬂed the country in 1995 amid corruption charges, following a heated political battle with the opposition. A war with Peru erupted in early 1995 over boundary disputes in a remote region, but ended in 1999. In 2000, a coup led to the removal of President Mahuad and, after a shortlived junta failed to gain military support, Vice-President Noboa became president. As a result, the political risk rating had no trend and ranged from 53 to 66, with higher volatility after 1994. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.70 presents the four risk ratings, risk returns and volatility for Paraguay, which lost two-thirds of its adult males and much of its territory in the catastrophic War of the Triple Alliance from 1865 to 1870. Important areas were won in the Chaco War of 1932– 1935. The 35-year military dictatorship of Alfredo Stroessner was overthrown in 1989. Lacking signiﬁcant mineral resources, Paraguay is predominantly agricultural, with a large and informal re-export sector, and massive activity by micro-enterprises and urban street vendors. There is a large subsistence sector, and urban unemployment is high. Paraguay has vast hydroelectric resources, including the world’s largest hydropower station that is operated jointly with Brazil. The economy is dependent on agricultural exports, electricity generation and re-exporting to Brazil and Argentina, so that economic performance is vulnerable to weather conditions and the economic performance of these two countries. In general, economic risk rating had rising trends in 1984– 1993 and 1995– 1997, and falling trends in 1993– 1995 and 1997 – 2002, with substantial volatility prior to 1998. Owing partly to the ﬁnancial crisis in Argentina, economic risk rating fell to 60 in 2002. After a period of no trend, the ﬁnancial risk rating rose to 80 in 1992 and had no trend to 2002, with volatility dominated by a peak in 1986. The rating fell substantially in 1997– 1998, and reached the low 70s in 2002. Foreign investment remains low due to widespread corruption. While the economy had annual average growth of 3% in 1995– 1997, GDP per capita fell by almost one-third in the last decade. Paraguay’s poor economic growth has been attributed to political uncertainty, corruption, stagnant structural reform, substantial

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domestic and foreign debt, and poor infrastructure. The end of the Stroessner dictatorship in 1989 did not bring political stability as the army maintained a strong inﬂuence. As a result, political risk rating fell to the mid-40s in 1986, increased noticeably after 1989, ranged around 70 from 1991 to 1998, fell to 1999, rose to 2001 and fell to the high 50s by 2002, with noticeable volatility from 1990 to 1992 and after 1997. Overall, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings. The four risk ratings, risk returns and volatility in Figure 4.71 are for Peru, which has experienced a total economic transformation over the last decade due to major reforms. Peru now has large foreign reserves, a stable exchange rate and consistently low inﬂation. Driven by foreign direct investment, the economy grew strongly in 1994 – 1997 but stagnated from 1998 to 2001 due to natural disasters, global ﬁnancial turmoil and internal factors. The collapse of the government in 2000 and the resulting political instability deterred foreign investment. The new president took strong measures to attract investment. As a result, economic risk rating rose from 1989 to 2000, fell by almost 10 from 2000 to 2001 and rose to mid-70s by 2002, with higher volatility prior to 1994. The economy recovered strongly in 2002, with 5.2% growth. Risk premia on government bonds also decreased, reﬂecting investor optimism and ﬁscal restraint. Consequently, ﬁnancial risk rating rose by almost 50 from 1988 to 1998 and remained in the mid-70s until 2002, with little or no volatility after 1994. However, these beneﬁts have not yet penetrated the wider economy. Although employment has improved slightly, 59% of the population lives at or below the poverty line. The economy is vulnerable to ﬂuctuations in world mineral prices, and a lack of infrastructure has deterred trade and investment. There was a generally rising trend for political risk rating, with substantial falls in 1992 and after 2001, and consistently mild volatility. Peru returned to democratic rule in 1980 but experienced economic problems and the birth of a violent rebellion. President Alberto Fujimori’s election in 1990 led to a decade of economic transformation and signiﬁcant progress in curtailing guerrilla activity. Nevertheless, the authoritarian measures and economic stagnation in the late 1990s led to the collapse of the government in 2000, with Alejandro Toledo the new president in 2001. Despite economic progress, political intrigue and corruption remain serious concerns. The Toledo government has grown unpopular amid rising concerns that political turmoil could endanger ﬁscal and ﬁnancial stability. Overall, the composite risk rating closely reﬂects the trends and volatility in the ﬁnancial and political ratings. The four risk ratings, risk returns and volatility for Uruguay are given in Figure 4.72. Uruguay’s wealthy economy is characterized by an

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export-oriented agricultural sector, a well-educated workforce and advanced welfare system. Although lacking major natural resources, Uruguay has traditionally been wealthier than most other countries in South America, with one of the lowest poverty rates. Tourism is becoming an increasingly important industry, while substantial earnings are being generated from offshore banking. With substantial state involvement in the economy, privatization is still widely opposed. Economic risk rating had a slightly increasing trend, with a consistent clustering of volatility. After an average 5% annual growth during 1996 –1998, in 1999– 2002 the economy suffered a major downturn, largely due to the spillover effects from Argentina and Brazil, the two main export markets and tourism sources. Financial risk rating rose consistently until 1997, when it fell by almost 20. After 1997, the rating increased slightly to 1999 and decreased from 2001 to 2002, with little associated volatility, apart from a peak in 1997. The 2001– 2002 withdrawals of deposited dollars by Argentina led to a plunge in the peso, a serious banking crisis, a massive rise in unemployment, a substantial fall in GDP, and falls in both economic and ﬁnancial risk ratings. Inﬂation surged and the burden of external debt doubled. However, ﬁnancial co-operation with the IMF and USA helped foster the fragile recovery. The government’s strategy to stimulate growth is based on increasing exports to partners in Mercosur, the European Union and North America. Uruguay enjoys an encouraging investment climate, with a strong legal system and open ﬁnancial markets. Apart from the violent Marxist urban guerrilla movement, the Tupamaros, in the late 1960s, Uruguay experienced repressive military control from 1973 to 1985. In general, Uruguay’s political and labour conditions have been among the most free on the continent. There was a generally increasing trend in the political risk rating, with some volatility clustering throughout the sample. Overall, the composite risk rating is less volatile than the economic and political risk ratings, and reﬂects their trends. The four risk ratings in Figure 4.73 are for Venezuela, one of the most urbanized countries in Latin America. Though wealthy in oil, coal, iron ore, bauxite and gold, up to 85% of Venezuelans live in poverty. The oil sector has dominated the economy since the 1920s, typically contributing more than 50% of government revenue, more than 80% of exports and around 25% of GDP. However, lack of structural reforms, an overdependence on oil and unstable oil prices led to ﬂuctuations in the ﬁscal accounts and GDP growth, and to repeated crises. Lower oil prices led to a depression, with cuts in welfare spending until 1989, as the economic risk rating rose until 1985 but fell signiﬁcantly to 1989, with high volatility. The rating remained volatile and rose to 1991 amid an austerity program and IMF support. Rising social and political upheavals deepened the

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recession and lowered the rating to 1999, with high volatility from 1998 to 1999. As rising oil prices fuelled the economic recovery from the deep recession in 1999, the rating rose from 50 to the high 70s in 2000, with volatility peaks, after which it fell as a weak non-oil sector, capital ﬂight, and a temporary fall in oil prices weakened the recovery. There was a generally increasing trend in the ﬁnancial risk rating, with associated volatility and a peak in 1997, despite repeated currency crises. In early 2002, conversion to ﬂoating exchange rates led to a steep depreciation of the Bolivar and a small positive impact on the rating. The political risk rating had a generally declining trend throughout the sample, with mild volatility, and peaks in 1984, 1992 and 2001. Venezuela became a democracy with the overthrow of General Marcos Jimenez in 1958 and the withdrawal of the military from involvement in national politics. Two parties dominated politics until the mid-1990s, but frequent economic crises and corruption scandals led to a decline in their support in the 1980– 1990s. Hugo Chavez, a former army ofﬁcer who promised to eradicate corruption and reform the public sector, was elected president in 1998. Political unrest and deep divisions remained, and he survived a coup attempt, and refused to resign and call early elections. Street protests and a general strike called by the opposition left Venezuela in economic, social and political turmoil by 2002. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the three component risk ratings. 4.3.7. Sub-Saharan Africa Figures 4.74– 4.99 present the four risk ratings, risk returns and the associated volatilities for the 26 Sub-Sahara countries, namely Angola, Botswana, Burkina Faso, Cameroon, Congo, Coˆte d’Ivoire, Democratic Republic of Congo, Ethiopia, Gabon, Ghana, Guinea, Kenya, Liberia, Malawi, Mali, Mozambique, Nigeria, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Uganda, Zambia and Zimbabwe. Figure 4.74 presents the four risk ratings, risk returns and volatility for Angola, which suffered a 27-year civil war after independence from Portugal in 1975. Angola is rich in natural resources, such as gold, diamonds, forests, ﬁsheries and oil reserves. The economy has grown strongly due to a major oil boom, but Angola remains very poor. Subsistence agriculture sustains 85% of the population, while oil accounts for over 60% of GDP and 80% of exports. Diamond mining is becoming increasingly important. Poor infrastructure and extensive landmines throughout the countryside have hindered agriculture, and food imports are essential. Despite internal conﬂicts, the economic risk rating rose from

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61 in 1985 to 85 in 1991, and fell to 63 in 1993– 1994. Following the 1994 peace accord, the rating rose to 77 in 1996, fell to 34 in 1997, remained low in 1998 as serious ﬁghting resumed, and rose after 1998 due to reforms. Little progress was made in addressing the persistent ﬁscal mismanagement and corruption. In 2000, Angola started an IMF-supported reform program. Progress has been made in reducing inﬂation, unifying exchange rates, and moving fuel, electricity and water prices closer to market rates, but there has been little progress in increasing foreign reserves and promoting greater transparency in government spending. Unlike the economic risk rating, ﬁnancial risk rating was low throughout the sample. After a period of no trend, the rating rose from 34 in 1996 to 60 in 1999, fell twice to 37 in 1990– 2000, rose to 58 in 2001 and fell to 44 by 2002, with noticeable volatility after 1996. A 1994 peace accord was signed between the government and the National Union for the Total Independence of Angola (UNITA). A national unity government was installed in early 1997, but serious ﬁghting resumed in late 1998, rendering hundreds of thousands of people homeless. Up to 1.5 million lives have been lost over the past quarter century. The death of the rebel leader, Jonas Savimbi, in 2002 raised hopes for peace. In April 2002, the Angolan army and UNITA signed a formal ceaseﬁre. Overall, the political and composite risk ratings rose and fell in a repeated pattern, but the composite risk rating was more volatile than the political rating. Figure 4.75 plots the four risk ratings, risk returns and volatility for Botswana. Owing to ﬁscal discipline, sound management and a cautious foreign policy, one of the world’s highest growth rates has been maintained since independence from Britain in 1966. Botswana may be the only African country to have matched the OECD in economic, ﬁnancial and political stability. Diamond mining has fuelled economic expansion and accounts for more than 30% of GDP and 90% of exports. Tourism, subsistence farming and cattle are other key sectors. Botswana’s impressive economic performance is threatened by high unemployment and poverty, and the expected downturn in diamond mining production. Unemployment is as high as 40%. HIV/AIDS infection rates are the world’s highest, even though the government is ﬁghting a battle against the epidemic. Annual growth averaged 9% from 1966 to 1999. As expected, the economic risk rating ranged from 81 to 91 from 1985 to 2000, with consistent and substantial volatility, but had fallen to 77 by 2001 and rose to 78 in 2002. There was an upward trend in the ﬁnancial risk rating until late 1999, when it rose to 95. As for the economic risk rating, the ﬁnancial risk rating fell to 2001, rose to 2002 and reached 85 in 2002, with discernable volatility peaks. All exchange rate controls were permanently removed in 1999. The government has offered generous incentives to

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attract foreign investment, maintain budget surpluses, have no domestic debt and insigniﬁcant foreign debt, and has accumulated large foreign reserves that represent 39 months of imports. Formerly the British protectorate of Bechuanaland, Botswana adopted its new name upon independence. Four decades of civilian leadership, progressive social policies and signiﬁcant capital investment have created one of the most dynamic economies in Africa. As a result, the political risk rating rose from 64 in 1984 to 79 in 2001, with a noticeable volatility clustering throughout the sample. As for the economic and ﬁnancial risk ratings, the political risk rating fell slightly after 2001. As an average measure of country risk, the composite risk rating reﬂects the trends and volatility in both the ﬁnancial and political risk ratings. Figure 4.76 plots the four risk ratings, risk returns and volatility for Burkina Faso, formerly Upper Volta, one of the world’s poorest countries, with limited natural resources, fragile soil and severe income inequality. Drought, poor infrastructure, high illiteracy rate and vulnerability to external shocks are persistent problems. About 90% of the population depends on subsistence agriculture. Much of the labour force has migrated to neighbouring countries such as Ghana and Coˆte d’Ivoire for work. Cotton and remittances are the two main sources of foreign exchange. Exports are sensitive to ﬂuctuations in world prices, while unproﬁtable state-controlled ﬁrms dominate. However, since the devaluation of the CFA Franc in 1994, a series of World Bank and IMF supported economic reforms were launched, based on trade liberalization, privatization and ﬁscal discipline. Growth was higher than 5% from the late 1990s to 2002, but it did not have a signiﬁcant impact on poverty. The economic risk rating rose to 74 by 1991 and fell to 57 by 2002, with greater volatility after 1995. Economic progress depends on lower inﬂation and trade deﬁcits, higher private investment, development of mineral resources, infrastructure investments, more competitive agricultural and livestock sectors, and stable food supplies and prices. International aid remains crucial, and Burkina Faso was one of the ﬁrst beneﬁciaries of the World Bank and IMF debt-relief and poverty reduction programs. At least 20% of the government budget is ﬁnanced from international aid, and the majority of infrastructure investments are externally ﬁnanced. Financial risk rating rose to 1997, fell substantially in 1997, rose to 75 by 1999 and had a steep decline to 2002, with little or no volatility, apart from ﬁve peaks from 1997 to 2001. Burkina Faso won independence from France in 1960, and was given its new name by Thomas Sankana, who came to power after a coup in 1983. Military coups during the 1980s were followed by the establishment of a multiparty system in the early 1990s. Political risk rating fell to 42 by 1991 and rose to 66 by 2002, with mild volatility.

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Overall, the composite risk rating closely reﬂects the trends and volatility in the ﬁnancial and political risk ratings. Figure 4.77 plots the four risk ratings, risk returns and volatility for Cameroon, one of the richest primary commodity economies in SubSaharan Africa. For 25 years following independence in 1961, when the former French Cameroon and part of British Cameroon merged, Cameroon has been one of Africa’s most prosperous countries. The fall in commodity world prices for its principal exports of oil, cocoa, coffee and cotton in the mid-1980s, combined with an overvalued currency and economic mismanagement, led to a decade-long recession. Per capita GDP fell by more than 60% from 1986 to 1994, current account and ﬁscal deﬁcits increased, and foreign debt grew. Since 1990, various World Bank and IMF reforms were launched to spur business investment, increase efﬁciency in agriculture, improve trade and recapitalize banks. Measures such as the 65% cut in civil service salaries in 1993 have been painful. Economic risk rating had no trend to 1993, but fell substantially in 1994 with the devaluation of CFA franc, the common currency of Cameroon and 13 other African states. After 1994, there was a rising trend, reaching 76 in 2002, with periodic volatility. In June 2000, the government completed an IMF-supported 3-year structural reform program to reduce poverty and improve social services. More reforms are necessary, including increased budget transparency, privatization and poverty reduction. Financial risk rating had a falling trend to 1994, rose to 67 in 1999, fell to 53 in 2000 and rose to 65 in 2002, with a clustering of volatility. Cameroon has generally enjoyed political stability, which permitted the development of agriculture, health care, education, transport and the oil industry. In general, the political risk rating rose to 1991, fell to 1994 and followed a rising trend to 2002, with volatility concentrated in 1984 and 1991– 1997. Despite moving toward democratic reforms, there is limited freedom of expression, with political power remaining in the hands of an ethnic oligarchy. A brief border war was fought with Nigeria in 1996 over the disputed oil-rich Bakassi Peninsula. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.78 plots the four risk ratings, risk returns and volatility for the Republic of Congo. The former French region of Middle Congo became the Republic of Congo after independence in 1960. Oil production accounts for more than 50% of GDP and large shares of government revenues and exports. Rising oil revenues ﬁnanced major development projects in the early 1980s. Annual growth averaged 5%, one of Africa’s highest. Congo’s economy underwent a difﬁcult transition from 1994 to 1996. Substantial progress was achieved due to improved public ﬁnances,

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external debt restructuring and a fall in personnel expenditures. The economic recovery was interrupted by the 1997 war, the armed conﬂict in 1998– 1999, and the fall in the world oil prices. There was a slight rising trend in the economic risk rating, apart from 1997 to 1998 when it fell by 38 due to the civil war. The rating remained 32 and rose to pre-war levels until late 1998. Apart from two volatility peaks, there is very little volatility. The government has continued with economic reforms amid a tense internal climate. President Sassou-Nguesso has focused on improved governance, economic reforms, privatization and cooperation with international ﬁnancial institutions. New investment laws were adopted to attract foreign investment. Despite successful economic and ﬁnancial reforms, Congo offers limited incentives to new investors. With no trend to 1992, ﬁnancial risk rating rose to 65 in 1996, fell to 32 from 1997 to 2002 and rose to 59 by 2002. There is little volatility, apart from the 1994 peak associated with the devaluation of the African franc, and numerous peaks from 1997 to 2000. A quarter century of Marxist politics were abandoned in 1990 and a multi-party system was installed in 1992. Former President Denis Sassou-Nguesso returned to power in 1997 after a brief and bloody civil war, in which he received support from Angola. Ethnic unrest plagued Congo after 1997. The political risk rating rose consistently after the 1992 elections, fell substantially in 1997 and 1999, and rose to 57 by 2002, with little volatility, apart from a peak in 1997. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political ratings. Figure 4.79 gives the four risk ratings, risk returns and volatility for Coˆte d’Ivoire, the third largest economy in Sub-Saharan Africa and the dominant force in the West African Economic and Monetary Union, which has the CFA franc as a common currency. The economy is market based, with agriculture employing 68% of the population. As the world’s largest cocoa producer, and a major producer of coffee and palm oil, it is highly sensitive to changes in world commodity prices and weather conditions. After a poor performance in the 1980s and early 1990s, the economy recovered in 1994 due to the 50% devaluation of the CFA franc, higher commodity prices, higher non-traditional primary exports, trade and banking liberalization, offshore oil and gas discoveries, and external ﬁnancing and debt rescheduling by international lenders and France. Adherence to donor-mandated reforms led to growth of 5% in 1996 – 1999, but economic conditions deteriorated through a fall in commodity prices, rise in fuel costs and a military coup in 1999. As a result, economic risk rating fell to 42 by 1991 and rose to 77 by 1999, with mild volatility, fell to 64 in 2001 and rose to 69 in 2002. On the other hand, ﬁnancial risk rating fell substantially to 2000 and rose to 55 by 2002, with signiﬁcant volatility

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after 1996. Coˆte d’Ivoire won independence from France in 1960. Political stability of 40 years was shattered by the military coup in December 1999, the ﬁrst in its history. The military junta led by General Guei overthrew the government of President Bedie. Elections in late 2000 excluded prominent opposition leader Ouattara, resulting in Guei’s victory. Violence spread and many Ouattara supporters were killed after calling for new elections. A popular uprising against the election results forced Guei to ﬂee and brought Gbagbo to power. A return to political stability is critical for Coˆte d’Ivoire to realize its economic potential. Prior to the 1994 military coup, political risk rating ranged from 59 to 69, with virtually no volatility, fell by 15 in 2000, rose to 58 in 2001 and fell to 54 in 2002, with a volatility peak in 1990– 2001. Overall, the composite risk rating reﬂects the trends in the economic and political risk ratings, but is less volatile than the economic rating. Figure 4.80 plots the four risk ratings, risk returns and volatility for the Democratic Republic of Congo (DRC), which is rich in natural and mineral resources, however, mineral wealth has declined since the mid-1980s. The fortunes of DRC, one of the world’s poorest nations, have been dominated by civil war and corruption. The war began in mid-1998 and dramatically reduced GDP and government revenue, increased external debt, and led to 3.5 million deaths from ﬁghting, famine and disease. Foreign investment fell due to political uncertainty and lack of infrastructure. In 2001, IMF reforms, such as liberalization of oil prices and exchange rates, and ﬁscal and monetary reforms, were launched. Agriculture is the main economic sector, while industry is underdeveloped relative to its potential. Diamonds dominate exports, accounting for about 50% of export receipts. The economic risk rating ranged from 15 to 62, reaching 41 by 2002. There was no trend but high volatility from 1997 to 1999, during which the rating fell substantially. Unlike the economic risk rating, ﬁnancial risk rating fell to 22 by 1991 and rose to 54 by 2002, with substantially higher volatility after mid-1997. After independence in 1960 from Belgium, army chief Mobutu came to power in 1965. As President Mobutu Sese Seko, he renamed the country Zaire and sought USA support by facilitating the operations against the Soviet-backed Angola. In 1997, the country was invaded by Rwanda in a ﬁght against the Hutu militias. The invasion encouraged the anti-Mobutu rebels, who installed Kabila as president and renamed the country. Kabila’s regime was challenged in 1998 by a new rebellion, backed by Rwanda and Uganda. Troops from Zimbabwe, Angola, Namibia, Chad and Sudan intervened to support Kabila’s regime, as each had signiﬁcant economic interests in DRC. A ceaseﬁre was signed in 1999, but irregular ﬁghting continued. Kabila was assassinated in early 2001 and replaced by his son. The political risk rating has generally been

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low, ranging from 18 to 46, with virtually no volatility, apart from three peaks in 1993 and 1997 – 1998. Overall, the composite risk rating reﬂects the trends in the economic, ﬁnancial and political risk ratings, but is less volatile than the ﬁnancial rating. Figure 4.81 gives the four risk ratings, risk returns and volatility for Ethiopia, an extremely poor country. Drought, famine, war and poor economic policies in the 1970s and 1980s led to the starvation of millions. With the 1991 overthrow of the military junta, political and economic conditions stabilized, but investor conﬁdence deteriorated during the 1998– 2000 war with Eritrea. As a result, economic risk rating had no trend until 1993, rose to 75 by 1997, fell to 60 by late 1998 and rose slightly by 2002, with little volatility after 1998. Financial risk rating fell to 26 by 1992, rose to 74 prior to the 1998 war, which led to a fall of 25, and had a rising trend after the war, with little volatility but some peaks. The economy is based on agriculture, which accounts for 50% of GDP, 80% of exports and 85% of employment. Coffee is the main export, followed by hides and skins, pulses, oilseeds and khat, while sugar and gold are becoming important. The government owns all land, hampering growth in the industrial sector as entrepreneurs cannot use land as loan collateral. Given the economic conditions, provisions for drought relief, development planning and crucial imports, such as oil, have largely been covered through foreign assistance. Ethiopia qualiﬁed in 2001 for debt relief from the Highly Indebted Poor Countries initiative. The current government has embarked on cautious economic reforms, including privatization and government deregulation, but has failed to attract foreign investment. Unlike other African countries, Ethiopia remained free from colonial rule, except for Italian occupation in 1936– 1941. In 1974, a military junta deposed Emperor Selassie, who had ruled since 1930, and established a socialist state, but was overthrown in 1991 by a rebel coalition. A constitution was adopted in 1994 and the ﬁrst democratic elections were held in 1995. A border war of over 2 years with Eritrea ended in late 2000, but a settlement has not been ﬁnalized due to the refusal by Ethiopia to surrender sensitive territory. As a result, political risk rating fell to 18 by 1992, rose to 66 by 1997 and fell to 55 by 2002, with substantial volatility in 1992– 1993. Overall, the composite risk rating reﬂects the trends and volatility in the ﬁnancial and political risk ratings. Figure 4.82 plots the four risk ratings, risk returns and volatility for Gabon, one of the most prosperous and stable African countries. The economy depended on timber and manganese until offshore oil reserves were discovered in the early 1970s. Oil revenues account for 65% of government spending, 43% of GDP and 81% of exports. Despite the natural wealth, the economy suffers from poor ﬁscal management. Oil production

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has declined rapidly, with little planning for the after-oil scenario. The IMF provided a 1-year standby arrangement in 1994 –1995, a 3-year Enhanced Financing Facility in late 1995, and a large stand-by credit in late 2000. France provided additional ﬁnancial support in 1997. However, overspending on off-budget items, the 1986 oil price shock, the 1994 CFA franc devaluation, low oil prices in the late 1990s and slow reforms led to serious debt problems. The 1999– 2000 oil price rise helped growth, but a slowdown in production hampered potential gains. In late 2000, a new agreement was signed with the Paris Club to reschedule ofﬁcial debt. Economic risk rating fell in 1984 – 1987, 1992 –1994, 1999– 2000 and 2001– 2002, and rose in 1988 –1992, 1994– 1999 and 2000– 2001, with a clustering of volatility. Financial risk rating rose from 59 in 1984 to 74 in 1999, fell to 61 in late 2000 and rose to 71 in 2002, with discernable volatility, especially after 1996. Overall, short-term progress depends on a buoyant world economy and IMF-mandated reforms. Since independence from France in 1960, Gabon has had only two autocratic presidents. Despite comprising more than 40 ethnic groups, Gabon has escaped the conﬂicts of other West African states owing to its prosperity and the continuing presence of French troops. Dependence on oil has made economic and political stability vulnerable to oil price ﬂuctuations. The fall in oil prices in the late 1980s led to rising opposition to President Bongo and demonstrations in 1990. President Bongo introduced a multiparty system and a new constitution in 1991. Political risk rating fell to 55 by early 1993, rose to 64 in 2001 and fell to 60.5 in 2002, with several volatility peaks. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political ratings. Figure 4.83 gives the four risk ratings, risk returns and volatility for Ghana, which was the ﬁrst sub-Saharan nation to gain independence in 1957. Rich in natural resources, Ghana has double the per capita income of the poorer countries in the region, but is still heavily dependent on international ﬁnancial support. Gold, timber and cocoa are major exports, and the industrial base is relatively advanced. Tourism has become one of the largest sources of foreign exchange. Economic activity is based on subsistence agriculture, which accounts for 35% of GDP and employs 60% of the work force. Following the 1983 IMF economic recovery program, economic risk rating had a rising trend from 1984 to 1990, and followed no trend after 1986, ranging from 50 to 60, with mild volatility and two peaks in 1984 and 1996. During 1984 – 1988, the economy grew strongly due to renewed exports, aid inﬂows and a foreign exchange auction. Infrastructural repairs, improved weather and producer incentives revived output in the early 1990s. In 1987, the IMF approved a 3-year extended fund facility. From 1987 to 1990, the recovery program concentrated on

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restructuring and revitalizing social services. The third phase began in early 1998, focusing on ﬁnancial transparency and economic stability. In 2002, Ghana obtained debt relief under the Heavily Indebted Poor Country program. Thus, there was a generally rising trend in ﬁnancial risk rating to 1998, after which it fell to 2001 and rose to 2002, with volatility clustering prior to 1986 and after 1997. Despite its mineral resources, good education system and efﬁcient civil service, Ghana suffered from corruption and mismanagement soon after 1957. A new constitution, allowing a multiparty system, was approved in 1992. Flight Lieutenant Jerry Rawlings, Head of State since the coup in 1981, won presidential elections in 1992 and 1996, but was prevented from running for a third term in 2000. Ghana’s stable politics, openness and one of the lowest crime rates in the world, has attracted business visitors and tourists. There was a generally rising trend in political risk rating, with a noticeable clustering of volatility. Overall, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings. The four risk ratings, risk returns and volatility in Figure 4.84 are for Guinea. While rich in mineral resources, Guinea is among the poorest in West Africa. Guinea owns over 30% of the world’s bauxite reserves and is the second-largest producer. Diamonds and gold are also exported on a large scale. Economic reforms in 1985 were launched to eliminate agriculture and foreign trade restrictions, strengthen the exchange rate, increase education spending and reduce government bureaucracy. In 1996, major reforms were adopted to restore the private sector, promote investment, reduce state control, and improve ﬁscal and judicial frameworks. Moreover, under 1996 and 1998 IMF and World Bank agreements, Guinea continued ﬁscal reforms and privatization, and shifted expenditures and internal reforms to the education, health, infrastructure, banking and justice sectors, but cabinet changes in 1999, corruption, economic mismanagement and large government spending since 2000 slowed the reforms. In 2002, the IMF suspended support due to a failure to meet targets. Spending in targeted social sectors and defence contributed to a large ﬁscal deﬁcit. Economic risk rating had a generally rising trend to 1999 and was virtually ﬂat to 2002, with higher volatility prior to 1996. Financial risk rating generally had a rising trend, reaching 80 by 2002, but was more volatile after 1996. Economic progress depends on policy reforms and private sector responses. Corruption and political instability continue to dampen foreign investment. Guinea won independence from France in 1958 and did not hold democratic elections until 1993, when General Lansana Conte, head of the military government, was elected president, and re-elected in 1998. Unrest in Sierra Leone and Liberia spilled over into Guinea on several occasions, threatening political stability

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and creating humanitarian emergencies. Political risk was very high until the 1998 elections, with the risk rating ranging from 45 to 51, after which it rose to high 50s in 1999, fell by 2000, rose in 2001 and fell to 50 by 2002, with a discernable clustering of volatility after 1995. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political ratings. The four risk ratings, risk returns and volatility in Figure 4.85 are for Kenya, a major trade and ﬁnancial centre in East Africa. Kenya is a major African safari destination, but tourism has been heavily affected by fear of terrorism. The economy declined from 1974 to 1993, due to inept agricultural policies, inadequate credit and poor terms of trade. As a result, a major IMF and World Bank reform program began in 1993, leading to the removal of price, foreign exchange and import controls, privatization of state ﬁrms, reduction of government bureaucracy, and conservative ﬁscal and monetary policies. From 1994 to 1996, the economy recovered and average annual GDP growth exceeded 4%. However, growth fell in 1997 due to adverse weather conditions and slower economic activity. In 1997, the IMF suspended support due to the failure to meet reform targets. From 1999 to 2000, a severe drought led to negative growth in 2000. The IMF resumed loans in 2000, but suspended lending in 2001 when the government failed to meet anti-corruption measures. Despite strong rains, weak commodity prices, corruption and low investment led to modest growth in 2001– 2002. Reﬂecting these conditions, economic risk rating fell to high 40s by 1993, rose to 70 in 1996 and had a falling trend to 2002, with noticeable volatility. Financial risk rating fell to high 40 by 1992, rose to low 70s by 1997, with noticeable volatility, but it was volatile and had no trend from 1997 to 2002. Kenya’s ethnic diversity has been a source of conﬂict. Political risk rating varied between 47 and 68, with rising trends in 1984– 1985, 1987– 1988, 1992 – 1995 and 2000 –2001, and a noticeable clustering of volatility. Kenya won independence from Britain in 1963 and was led by the liberation idol Kenyatta until his death in 1978. He was succeeded by President Moi. The ruling Kenya African National Union was the sole legal party in 1980s. A multiparty system was established in the early 1990s amid violent protests and international pressure. The opposition failed to remove Kanu from power in the 1992 and 1997 elections, which were marred by violence and fraud. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.86 presents the four risk ratings, risk returns and volatility for Liberia, Africa’s oldest republic, which became independent in 1847. Prior to the civil war of 1989– 1996, economic prospects were promising due to vast natural resources and a favourable climate. However, the war

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devastated the economy, with major businesses destroyed and most foreign investors departing. Economic restoration was initiated after the 1997 elections. Timber and rubber have been the main exports since the end of the war, and alluvial diamond and gold mining are also important. Few foreign investors have returned after the war due to the depressed business climate and continuing instability. Economic risk rating fell from 53 in 1984 to 14 in 1992, rose to 70 by 2000 and fell slightly to 2002, with little volatility apart from peaks in 1990– 2000. Liberia’s business sector is controlled by foreigners, mainly from Lebanon and India. Historically, Liberia has relied on foreign assistance but, given serious corruption and disregard for human rights, it has declined drastically. Signiﬁcant aid continues to arrive from Western countries via international aid agencies and non-governmental organizations. Financial risk rating fell consistently to 1990, remained 16 until the 1997 elections and rose to 35 by 2002, with virtually no volatility, apart from a peak in 1997. In the 1990s, the long civil war and Liberia’s role in the war in Sierra Leone attracted international attention. President Taylor, who won the 1997 elections after the end of the war, was unable to eliminate rebel groups. In 1999, Taylor was accused of supporting rebels in Sierra Leone, while he accused Guinea of supporting Liberian rebels in the north. In 2000, government forces battled rebels and engaged in border skirmishes with Guinean forces, resulting in the displacement of thousands of people. The United Nations imposed sanctions in 2001 for Liberia’s support of the rebels in Sierra Leone. Liberia’s political risk remained very high during the sample. Political risk rating fell to 9 in 1990 and rose to 42 by 2002, with two peaks dominating the volatility. Overall, the composite risk rating reﬂects the trends in the economic and political risk ratings, but is less volatile than that of the political risk rating. Figure 4.87 plots the four risk ratings, risk returns and volatility for Malawi, one of the world’s least developed countries. The economy is based on agriculture, with tobacco, tea and sugar comprising more than 85% of exports. About 90% of the population is rural, and the majority is subsistence farmers. Small farm sizes, poor soil fertility and lack of inputs cannot meet domestic food demand. As a result, the economy is vulnerable to external shocks, such as declining terms of trade and natural disasters. Malawi has depended heavily on ﬁnancial support from the IMF, World Bank and individual donor countries since 1981, but without lasting structural reforms. Interest rates and inﬂation remain high. Since 1998, the kwacha has depreciated but remained fairly stable due to adequate foreign exchange reserves and tight control of the money supply. In late 2000, Malawi was approved for debt relief under the Heavily Indebted Poor Countries program. The government faces strong challenges in developing

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a market economy, improving educational facilities, dealing with environmental problems, growing population and HIV/AIDS epidemic, and maintaining ﬁscal discipline. Foreign investment is crucial for agriculture, horticulture, agro-processing, tourism, manufacturing and mining. As a result, economic risk rating varied substantially, ranging from 42 to 63 and without trend. Prior to 1997, ﬁnancial risk rating was almost ﬂat in the mid-50s, after which it varied substantially, reaching 61 in 2002. Established in 1891, the British protectorate of Nyasaland became the independent nation of Malawi in 1964. After three decades of one-party rule, multiparty elections were held in 1994. A provisional constitution came into effect in 1995. Current President Muluzi came to power at the 1994 elections, and was re-elected in 1999. His attempts to amend the constitution to permit a third term were unsuccessful. Corruption remains a major problem. Political risk was generally high, particularly prior to 1994, and the risk rating rose to 75 by 1997 but fell after the 1999 elections, with a noticeable clustering of volatility and peaks. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political ratings. Figure 4.88 plots the four risk ratings, risk returns and volatility for Mali, one of the world’s 10 poorest countries. The Sudanese Republic and Senegal won independence from France in 1960 and established the Mali Federation. Senegal withdrew after a few months and the remaining Sudanese Republic was renamed Mali. The economy has been heavily affected by droughts, rebellions, a coup and 23-year military dictatorship. Mali is mostly a desert, with high income inequality and economic activity concentrated around the Niger River. Agriculture underpins the economy, with cotton the main export, while mining has recently accounted for 36% of export earnings. After Ghana, Mali is the second largest gold producer in West Africa. Industrial activity consists of farm commodity processing. Signiﬁcant economic progress has been made, with support from the IMF and World Bank. Economic restructuring and privatization resulted in steady annual growth and low inﬂation in the 1990s. Mali beneﬁts from signiﬁcant international aid and membership of the West African Economic and Monetary Union countries, which share the CFA franc as a common currency. The economy is heavily dependent on foreign aid and is vulnerable to ﬂuctuations in world commodity prices. Adherence to economic reforms and devaluation of the CFA franc in 1994 led to a growth rate of 5% during 1996– 2002. Economic risk rating rose to high 78 by 1997, but fell to 48 by 2002, with some peaks in volatility. There was a structural break in the ﬁnancial risk rating in 1997, prior to which it was virtually ﬂat in the low 30s. The rating rose by 21 in 1997 and varied in the mid-60s until 2002. Overall, Mali is a relatively peaceful country. Military

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ruling was brought down in 1991 and the ﬁrst democratic elections were held in 1992. After his re-election in 1997, President Konare continued to support political and economic reforms and to ﬁght corruption. He stepped down in May 2002 and was succeeded by Toure. As a result, political risk rating rose from 38 in 1984 to 77 in 1999 and fell to 59 in 2002, with slightly greater volatility prior to the peak in 1992. Overall, the composite risk rating reﬂects closely the trend and volatility in the political rating. Figure 4.89 plots the four risk ratings, risk returns and volatility for Mozambique, which won independence from Portugal in 1975 and sustained a brutal civil conﬂict from 1977 to 1992. Almost 1 million people died from ﬁghting and famine, and the economy suffered heavily. Total production collapsed from 1981 to 1986. In 1987, reforms were launched to stabilize the economy. The IMF support under the Structural Adjustment Facility from 1987 to 1990 was followed by two arrangements under the Enhanced Structural Adjustment Facility (ESAF). A third ESAF arrangement was approved in mid-1999. Mozambique experienced an annual growth rate of 11% from 1997 to 1999 and rising foreign direct investment. Despite political stability and economic progress, the country remains very poor and still relies on international donors. The banking sector remains conservative, with high real interest rates, despite low inﬂation in the late 1990s. The mainly subsistence economy suffered severely from ﬂoods in early 2000– 2001, and from drought in 2002. As a result, economic risk rating rose by almost 50 during 1985– 1999, fell to 51 by 2002 and was more volatile prior to 1998. There is a slightly rising trend in the ﬁnancial risk rating, with higher volatility after 1997. Trade and investment reforms, reduction in red tape, and improved business and legal environments are necessary to diversify the export base. After independence, the country was drawn into the struggle against white rule in Rhodesia and South Africa. When Rhodesia became independent as Zimbabwe in 1980, the Renamo rebels were used by South Africa’s military intelligence to force Mozambique into expelling exiled South African dissidents. The resulting civil conﬂict left a legacy of landmines and amputees. The ruling party formally abandoned Marxism in 1989, and a new constitution in 1990 introduced multiparty elections and a free market economy. A peace agreement with rebel forces ended ﬁghting in 1992, leading to political stability and economic growth. Apart from 1989 to 1992, political risk rating had a generally increasing trend, with volatility observed in 1988– 1995. Overall, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings. The four risk ratings, risk returns and volatility in Figure 4.90 are for the oil-rich country of Nigeria, heavily affected by political instability, corruption, inadequate infrastructure and poor economic management.

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Former military rulers failed to diversify the economy away from the oil sector, which provides 20% of GDP, 95% of foreign exchange earnings and around 65% of budget revenues. The subsistence agricultural sector cannot support the rapidly growing population, the largest in Africa. After an IMF stand-by agreement in mid-2000, Nigeria received a debtrestructuring deal from the Paris Club and IMF credit. However, after withdrawing from the IMF program in 2002 by failing to meet reform targets, Nigeria became ineligible for further debt renegotiation. The government has lacked the political will to implement market-based reforms. In addition to the depression of the non-oil sectors, the informal sector has grown rapidly. Agriculture has suffered from mismanagement but remains an important sector, accounting for over 41% of GDP and 70% of employment. By 2002, GDP per capita fell to around 25% of mid-1970s levels. Foreign investment is low due to security concerns and poor infrastructure. Consequently, the economic risk rating varied substantially, ranging between high 40s and high 70s. Financial risk rating had a generally upward trend, rising by almost 40 over the sample, with noticeable volatility prior to 1988 and after 1996. Following nearly 16 years of military rule, a new constitution was adopted and a peaceful transition to civilian government was completed in 1999. Political liberalization allowed militants from religious and ethnic groups to express their frustration with increasing violence. A former British Colony, Nigeria is one of the world’s largest oil producers, but the trade in stolen oil has fuelled violence and corruption in the Niger delta. The government faces a daunting task of defusing ethnic and religious tensions to achieve economic and political stability. Political risk remained consistently high, with the political risk rating varying between 38 and 57, with a noticeable clustering of volatility. Overall, the composite risk rating reﬂects the trends and volatility in the ﬁnancial and political risk ratings. The four risk ratings, risk returns and volatility in Figure 4.91 are for Senegal, one of the most economically and politically stable African countries. After Coˆte d’Ivoire, Senegal is the most industrially advanced former French West African country. The economy relies on the oil industry, manufacturing, mining, groundnut production and processing, ﬁshing and tourism. Senegal is a leading member of the West African Economic and Monetary Union, West African francophone common market and Economic Community of West African States. Following an economic contraction in 1993, a structural reform program was launched in early 1994 with the support of the IMF, World Bank and other creditors. The program was aimed at facilitating growth and development by reducing the state role in the economy, improving public sector management, enhancing private sector incentives and reducing poverty.

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Government price controls and subsidies have been removed and private activity accounts for 82% of GDP. Economic growth had an annual average of 5% after 1995, while inﬂation remained under 3%. However, Senegal faces problems of unemployment, trade union militancy, juvenile delinquency and drug addiction. As a result, economic risk rating had a generally rising trend over the sample, with little volatility after 1997. On the other hand, ﬁnancial risk rating was ﬂat at 57 for most of 1984 – 1995, after which it rose to 72 by 1998, fell to 59 in 1998 and rose to 72 in 2002, with substantial volatility after 1996. Independent from France since 1960, Senegal joined with The Gambia to form the confederation of Senegambia in 1982, which was dissolved in 1989. Since 1982, there have been periodical clashes between a southern separatist group and government forces. Senegal was the ﬁrst African country to introduce a multi-party political system. The 40-year Socialist Party rule ended in peace after free elections in early 2000. Political risk rating remained high during the sample, varying between 52 and 63, had a falling trend to 1990 and a rising trend to 2002, with consistent and noticeable volatility. Overall, the composite risk rating reﬂects the trends in the economic and political risk ratings, but is less volatile than the political risk rating. Figure 4.92 presents the four risk ratings, risk returns and volatility for Sierra Leone, an extremely poor nation with high income inequality. Despite vast mineral, agricultural and ﬁshery resources, the economic and social infrastructure is underdeveloped and heavily damaged by the 1991– 2002 civil war. About 70% of the population is engaged in subsistence agriculture, which accounts for 42% of GDP. Sierra Leone has one of the world’s largest deposits of rutile, a titanium ore, and the economy is based on the mining sector, with mineral exports, particularly diamonds, as the main foreign exchange earner. In 1980s and early 1990s, economic growth was low due to a declining mining sector and rising corruption. The slow economic activity was followed by a decade of economic destruction. Since the war ended, massive amounts of foreign assistance from multilateral and bilateral donors have aided economic recovery. The government encourages foreign investment, but business has been hampered by a shortage of foreign exchange, corruption and political uncertainty. Economic recovery depends on stability and foreign aid, which is essential to offset the severe trade imbalance and supplement government revenues. As a result, economic risk rating rose and fell repeatedly, varying between 17 and 61, with higher volatility from 1992 to 2000. On the other hand, the ﬁnancial risk rating fell to 32 by 1997, rose to 53 in 1997, fell to 16 in 1999 and rose to 44 by 2002, with substantial volatility after 1996. The 1991– 2002 civil war between the government and the Revolutionary United Front resulted in around 50,000 deaths

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and the displacement of more than 2 million people, many of whom are refugees in neighbouring countries. With the help of a large UN peacekeeping mission and Britain, Sierra Leone emerged from the decade-long civil war in early 2002. National elections were held in May 2002. Poverty, tribal rivalry and ofﬁcial corruption, which led to civil war, remain major concerns. As a result, political risk fell from 50 in 1985 to 25 in 1992, remained 25 until late 1994 and rose to 48 by 2002, with higher volatility after 1994. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. The four risk ratings in Figure 4.93 are for South Africa, a middleincome developing country with daunting economic problems inherited from the apartheid era, especially poverty and little economic power among disadvantaged groups. South Africa is productive and industrialized, with a high division of labour between the formal and informal sectors, and an uneven wealth and income distribution. A diverse manufacturing industry has made it a world leader in several specialized sectors, including railway rolling stock, synthetic fuels, and mining equipment and machinery. The Growth, Employment and Redistribution (GEAR) strategy was directed toward open markets, privatization and a favourable investment climate, but had mixed results. However, great progress has been made in restructuring the economic system, which was based on import substitution, high tariffs and subsidies, anti-competitive behaviour and government intervention. The declining trend in the economic and ﬁnancial risk ratings in 1994– 1999 was due to low and unstable growth, high unemployment, skyrocketing crime, corruption and HIV/AIDS. After 1999, the ratings started to increase as President Mbeki vowed to promote economic growth and foreign investment, and to reduce poverty by relaxing restrictive labour laws, increasing the pace of privatization, and reducing government spending. The economy slowed in 2001 as a result of the international downturn. South Africa has a sophisticated ﬁnancial structure, with a large and active stock exchange and mild exchange controls. The political risk rating fell to 1987, followed a generally increasing trend to 1997 and a declining trend thereafter, with discernable volatility throughout the sample. Ruled by a white minority until 1994, South African activists struggled for much of the last century before succeeding in overthrowing apartheid and extending democracy. After 1994, President Mandela’s leadership encouraged democratic reforms and reconciliation amid painful legacies of lawlessness, social disruption and lost education. Under the 1999 government of President Mbeki, economic transformation became a priority. Having emerged from the isolation of the apartheid era, South Africa has become an active player

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in international politics. Overall, the composite risk rating closely reﬂects the trends and volatility in the three component risk ratings. Figure 4.94 plots the four risk ratings, risk returns and volatility for Sudan, the largest country in Africa. In the last two decades, Sudan has suffered from a bitter civil war. The country is rich in cultivatable land, gold and cotton, but the oil industry underpins the economy. Agriculture employs 80% of the work force and accounts for 39% of GDP. Irrigation and transportation problems constrain the development of a more dynamic agricultural economy. Civil conﬂicts, adverse weather and low world commodity prices pose serious threats to the economy. Despite isolation from the international community, the economy has strengthened due to sound policies and infrastructure investments. After 1997, Sudan implemented IMF economic reforms. Crude oil exports began in 1999. Oil production revived light industry, expanded export processing zones and helped GDP growth. International isolation is slowly easing, but Sudan’s foreign debt remains high and largely un-serviced. Debt renegotiations can be initiated after a full peace deal is secured. Reﬂecting these conditions, economic risk rating fell to 21 by 1993 and rose to 68 by 2002, with consistent and noticeable volatility. Similarly, ﬁnancial risk rating fell to 18 in 1988 and rose to 62 by 2002, with a clustering of volatility after 1996. Military regimes have dominated Sudan since independence from Britain in 1956. Apart from an 11-year period of peace, 1972– 1982, Sudan has had a civil war between the Muslim north and the Animist and Christian south, due to the north’s economic, political and social domination of the south. Since 1983, more than 2 million people have died and over 4 million have been displaced. The ruling regime comprises a military elite and an Islamic party that gained power in a 1989 coup. Northern opposition parties have joined the southern rebels as part of an anti-government alliance. Peace talks gained momentum after 2001, with the signing of several accords. Sudan’s political risk was high in the sample, with the risk rating varying between 13 and 43. However, the political risk rating rose from 1993 to 2002, with a clustering of volatility. Overall, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.95 provides the four risk ratings, risk returns and volatility for Tanzania, one of the world’s poorest countries. Agriculture is the basis of the economy, accounting for 50% of GDP, 85% of exports and 80% of employment, but only 4% of the land area is cultivatable. As one of Africa’s smallest, the industrial sector accounts for about 10% of GDP and concentrates on production of raw materials, import substitutes and processed agricultural products. Structural reforms have been initiated to liberalize the economy and encourage foreign and domestic private

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investment. After 1986, state economic controls were removed to encourage private sector activity. Fiscal and monetary reforms resulted in a lower budget deﬁcit, depreciation of the exchange rate, trade liberalization, removal of price controls and ﬂexible interest rates. However, economic recovery and poverty reduction depend heavily on ﬁnancial support from the IMF, World Bank and bilateral donors. From 1991 to 2002, growth improved due to higher industrial production and mineral output, and oil and gas exploration. Consequently, economic risk rating had a generally rising trend over the sample, with greater volatility prior to 1989 and, after a low of 24 in 1998, rose to 69 in 2002. On the other hand, ﬁnancial risk rating rose steadily to 70 in 1997, with mild volatility, but fell to 29 by 1999 and rose to 41 by 2002, with substantial volatility. Despite the economic progress and political stability, foreign investment remains low. Unlike many other African states, Tanzania has been free from internal conﬂicts. Shortly after independence, mainland Tanganyika and the island of Zanzibar merged to form Tanzania in 1964. The constitution was amended in 1992 to allow for multiparty politics, and the ﬁrst democratic elections were held in 1995. Zanzibar’s semi-autonomous status and popular opposition have led to two contentious elections since 1995, both won by the ruling party despite claims of voting irregularities. As a result, political risk rating had a generally rising trend to 1995 but fell to 60 by 2002, with a clustering of volatility. Overall, the composite risk rating reﬂects the trends in all three component risk ratings, but is more volatile than the economic and ﬁnancial ratings. The four risk ratings, risk returns and volatility in Figure 4.96 are for Togo, a small sub-Saharan economy. Subsistence agriculture and commerce are the two main economic activities in Togo, and the capital, Lome, is an important regional trading centre. Cotton, cocoa and coffee comprise 40% of exports. Togo is the world’s fourth-largest phosphate producer, but output fell in 2002 due to power shortages and the costs of developing new deposits. The decade-long reform program supported by the IMF and World Bank has been slow in encouraging foreign investment and improving government budget. Economic progress depends on privatization, transparent government ﬁnancial operations, progress on legislative elections and continued support from foreign donors. Political unrest and international disapproval of the 1998 elections left investor conﬁdence fragile. Moreover, the government’s unwillingness to move to democracy has frozen most bilateral and multilateral aid. In general, economic risk rating rose and fell repeatedly, varying between 50 and 70, with a clustering of volatility. After a period of no trend, ﬁnancial risk rating rose from 49 in 1994 to 71 in 1998, fell to 60 in 2000 and rose to 69 in 2002, with higher volatility after 1996. French Togoland became

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independent as Togo in 1960, and General Gnassingbe Eyadema has been the military ruler since 1967. Despite multiparty elections instituted in the early 1990s, the government continues to be dominated by the president, whose Rally of the Togolese People party has maintained power since 1967, despite accusations of suppressing opposition and election fraud. Togo has also been accused of human rights abuses by international organizations. A joint panel by the UN and Organization of African Unity was established in 2000 to investigate allegations by Amnesty International that hundreds of civilians had been killed by armed government forces during the 1998 elections. Overall, political risk rating was low, varying between 35 and 56, but there was little volatile, with a falling trend to 1994 and a rising trend to 2002. Overall, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings. Figure 4.97 plots the four risk ratings, risk returns and volatility for Uganda, which has vast natural resources and considerable copper and cobalt deposits. Since the late 1980s, Uganda has rebounded from civil war and economic devastation to become a model of relative political stability and prosperity. Agriculture is the most important sector, accounting for over 80% of employment and nearly all the foreign exchange earnings, with coffee as the principal export. Since 1986, the government has introduced economic reforms supported by the IMF, World Bank and Western countries, which were designed to reduce inﬂation, boost exports, rehabilitate infrastructure, restore producer incentives, and improve resource mobilization and allocation in the public sector. During 1990– 2002, the economy performed strongly due to continued infrastructure investment, improved incentives for production and exports, lower inﬂation, better domestic security and the return of exiled Indian– Ugandan entrepreneurs. However, corruption and slowing reforms now threaten the sustainability of strong growth. In 2000, Uganda qualiﬁed for debt relief under the enhanced Highly Indebted Poor Countries and Paris Club arrangements. Despite progress since 1986, the economic risk rating decreased from 70 in 1989 to 10 by 1991, rose to 77 by 1997 and remained ﬂat in the mid-70s to 2002, with high volatility prior to 1994. However, ﬁnancial risk rating generally followed an upward trend, rising from 34 in 1986 to 71 in 2002, with a clustering of volatility. Uganda won independence from Britain in 1962. The dictatorial regime of Idi Amin from 1971 to 1979 was responsible for the deaths of over 300,000 opponents. Subsequent guerrilla war and human rights abuses under Milton Obote’s regime from 1980 to 1985 claimed another 100,000 lives. In 1986, Yoweri Museveni became president and introduced democratic reforms, which have been praised for substantially improving the human rights record and controlling abuses by the army and police. Consequently,

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the political risk rating generally followed an increasing trend, with substantial periods of volatility. Overall, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings. Figure 4.98 provides the four risk ratings, risk returns and volatility for Zambia, one of the world’s poorest countries. Once a middle-income country, Zambia began to slide into poverty after the 1970s, when copper prices declined and foreign borrowing increased due to falling revenues. An economic reform program was launched after democratic multi-party elections in 1991, with successful results in privatization, maintenance of positive real interest rates, elimination of exchange controls and endorsement of free market principles. However, over 70% of the population lives in poverty and social indicators continue to decline. The economy cannot sustain the rapid population growth, while the HIV/AIDS epidemic poses a serious threat to Zambia’s economic, political, cultural and social fabric. Unemployment is also a serious problem. An economic diversiﬁcation program is being pursued to reduce reliance on the copper industry, and to promote agriculture, tourism, gemstone mining and hydropower production. Zambia’s total foreign debt was very high when it qualiﬁed for Highly Indebted Poor Country Initiative debt relief in 2000, contingent upon meeting performance criteria. As a result, economic risk rating remained low but had a slight increasing trend, with noticeable and consistent clustering of volatility. There was no trend in ﬁnancial risk rating but discernable volatility, especially after 1998. In general, economic and ﬁnancial risk ratings varied between 30 and 60. The territory of Northern Rhodesia was taken over by Britain in 1923, and the name changed to Zambia upon independence in 1964. One-party rule ended with the 1991 elections, but subsequent elections in 1996 were tainted by abuse against opposition parties. In 2001, the ruling party candidate, Levy Mwanawasa, was elected president. Political risk was very high until early 1992. Democratic elections resulted in a signiﬁcant improvement in political risk, with the risk rating rising from 45 in 1992 to 74 in 1998. However, the rating fell to 56 by 2002, with little or no volatility, apart from a peak in 1992. Overall, the composite risk rating reﬂects the trends in the economic and political risk ratings, but is less volatile than the economic risk rating. The four risk ratings for Zimbabwe are given in Figure 4.99. Robert Mugabe, the ﬁrst prime minister and president since 1987, has been the sole ruler and dominated the political landscape since independence, but now presides over chaos, a land crisis and faltering economy. Under proper economic management, the wide range of natural resources should be able to support sustained economic growth. The country has large reserves of metallurgical-grade chromite and other commercial mineral deposits,

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and has long been the world’s third largest tobacco exporter. There was a generally declining trend in the economic and political risk ratings, with a noticeable clustering of volatility, while the ﬁnancial risk rating followed no trend but was highly volatile. Earlier moves to develop a marketoriented economy led to a reduction in the economic risk rating, with an associated volatility peak in 1992. The rating followed an increasing trend after 1992, but started to decline in 1997, with increasing volatility. Similarly, while the ﬁnancial risk rating varied substantially throughout the sample, its associated volatility was higher in the second half of the sample because IMF support was suspended due to a failure to meet budget targets. Moreover, the economy had been steadily weakened by excessive government deﬁcits, AIDS, rampant inﬂation and extreme income inequality. The government’s land reform program, characterized by chaos and violence, derailed the commercial sector, which had been a traditional source of exports, foreign exchange and employment. Politically, Zimbabwe had been improving, as shown by the increasing trend in the political risk rating to 1998. However, involvement in the war in the Democratic Republic of Congo, which began in 1998, contributed to domestic woes and caused the rating to enter a steep declining trend, reaching the mid-30s by 2002. In early 1999, Zimbabwe experienced considerable political and economic upheaval, and growing opposition against Mugabe’s regime. Local and international human rights monitors have noted a marked increase in human rights abuses since early 2000. Parliamentary elections in mid-2000 and presidential elections in early 2002 were associated with violent intimidation against opposition supporters, the press and judiciary. Overall, the composite risk rating closely reﬂects the trends and volatility in the economic and political risk ratings. 4.3.8. West Europe Figures 4.100– 4.120 present the four risk ratings, risk returns and the associated volatilities for the 21 West Europe countries, namely Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Malta, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey and United Kingdom. The four risk ratings for Austria are given in Figure 4.100. Austria experienced impressive growth after 1945, and is now one of the most developed countries in the world. In general, there are no trends for the four risk ratings, but there is a noticeable clustering of volatilities. The economic risk rating had a slightly declining trend until late 1986, with one peak in early 1986, followed by an increasing trend until 1997. During this

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period, the Austrian economy developed strongly, especially after 1995 when Austria became a member of the EU and implemented the EUimposed targets and reforms. In 1996, the economic risk rating increased by 10 points as a result of high growth, large capital inﬂows and low inﬂation, but the rating fell in 1997 and 1999 when the budget deﬁcit exceeded the EU target of 3%. After 1999, the rating increased as the budget deﬁcit decreased, but entered a downward trend from late 2001 as GDP slowed. There was a structural change for the ﬁnancial risk rating in 1997, prior to which the rating had little variation and some associated volatility. After 1997, the rating had two noticeable peaks in volatility in early 1999 and late 2000. The political risk rating started in the low 90s in 1984 and ended in the high 80s in 2002. There was a noticeable downward trend from 1991 to 1995, with high associated volatility. With EU membership in 1995, the coalition government collapsed over budget disagreements and strict adoption criteria for the European Monetary Union (EMU). A new coalition was formed in 1996, and the rating increased by 10 points. The rating remained generally ﬂat until the elections in 1999, when the far right Freedom Party under Joerg Haider was second with the centre right People’s Party. As a result, the political rating fell in 1999 and remained low in 2000 as the Freedom Party entered government following protracted coalition talks between the main parties and EU-imposed diplomatic sanctions on Austria. Haider resigned as leader of the Freedom Party in September 2000 and the EU ended 7 months of diplomatic isolation, after which the rating increased and remained high. As a weighted average of the three risk ratings, the trend of the composite risk rating and its associated volatility reﬂect those of the economic, ﬁnancial and political risk ratings. Figure 4.101 presents the four risk ratings for Belgium. The Belgian economy was one of the ﬁrst in Europe to industrialize and, apart from Luxembourg and Ireland, is the most open economy in the EU. As a result, the economic risk rating followed a generally increasing trend throughout the sample period, with some clustering of volatility. Belgium’s external trade in goods and services accounts for a large share of the GDP, with most of the international trade being conducted with Germany, France and the Netherlands. Foreign investment, especially from the USA, France and the Netherlands, has contributed signiﬁcantly to Belgian economic growth. The Belgian government has encouraged new foreign investment to promote employment. There was a noticeable structural change in the ﬁnancial risk rating in 1997, whereby it fell by almost 20 points. Prior to 1997, the ﬁnancial risk rating showed little variation and was very high, but the rating fell and remained in the high 70s for the rest of the sample period. There was a fall in the ﬁnancial risk rating with a corresponding

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volatility peak in 2000, which was associated with a large accumulated government debt as a percentage of GDP. Following this, the ﬁnancial risk rating entered an increasing trend due to the adoption of EU-imposed targets on the government debt. Belgium’s political risk rating varied substantially throughout the sample period, with substantial volatility. Conﬂicts between linguistic groups and political parties made it difﬁcult for Belgium to form stable coalition governments, with such conﬂicts leading to the fall of governments in the late 1980s and early 1990s. The general election in 1999 was a turning point in Belgian politics, with the political risk rating entering an increasing trend with high associated volatility, prior to which the rating increased and then decreased in a repeated pattern. The composite risk rating followed an increasing trend until 1996 with associated volatility, after which the rating displayed a decreasing trend followed by an increasing trend. Composite risk for Belgium reﬂects the trends and volatilities of the ﬁnancial and political risk ratings. The four risk ratings for Cyprus are presented in Figure 4.102. Cyprus has an open economy based on the service sector. Over the last two decades, the economy has shifted from agriculture to light manufacturing and services, with tourism contributing 70% to GDP. There was robust growth in the 1980s, especially after 1987, when the economic risk rating increased by almost 10 points. However, the performance from 1988 to 2002 has been mixed, as the rating decreased and then increased before falling by more than 10 points in 1997 as the trade deﬁcit increased substantially. This pattern reﬂected the economic vulnerability to swings in tourism arrivals and the need to restructure the economy. Declining competitiveness in tourism and manufacturing has led to economic reforms. The rating recovered in 1997, only to fall again to the low 70s due to the high trade deﬁcit. EU accession negotiations in 1998 led to an increasing trend until early 1999. From mid-2001 to 2002, the economic risk rating followed an increasing trend as economic performance improved. The ﬁnancial risk rating generally increased, with some variation to 1997, reaching the low 90s. Financial risk increased in 1997 and the rating fell by more than 10 points. However, the rating started an increasing trend in the same year as Cyprus revised its policy on foreign investment and passed a modern banking law, incorporating all the EU provisions for the prudential supervision of credit institutions. Political risk in Cyprus decreased over the sample period, with the rating following a generally increasing trend associated with high volatility. Political risk was very high in the 1980s as the tension between the Turkish and Greek Cypriots increased. In 1983, Turkish Cypriots declared an independent Turkish Republic of Northern Cypriots, which is recognized only by

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Turkey. From 1988 to 1992, the rating had an increasing trend which was followed by an absence of trend. Increasing tensions between the two groups have led to a vulnerable political scene. After the initiation of EU accession negotiations in 1998, the rating started an increasing trend as both groups followed EU membership aspirations. Political risk seems to have a signiﬁcant impact on the perceptions of country risk for Cyprus, as the composite risk rating reﬂects the trends and volatility in the political risk rating very closely. Figure 4.103 presents the four risk ratings for Denmark, which has one of the world’s highest standards of living. Like most Nordic economies, Denmark has a high ratio of state expenditure to total economic activity. The economic risk rating was highly volatile over the sample, with a noticeable structural change. Prior to 1992, the rating generally had a ﬂat trend around a level of 75. From 1992 to 1993, the rating was increasing, after which it remained generally ﬂat around 85. After the structural change, Denmark experienced high growth rates. Formerly high unemployment rates were more than halved, and public ﬁnance was in surplus. Financial risk rating showed no trend and little variation prior to 1997, after which it decreased and then increased, with a noticeable peak in volatility in early 2001. Denmark remained outside the EMU when a referendum voted against it in 2001. The political risk rating decreased until early 1987, but remained generally ﬂat until 1992. A decreasing trend for 1992 – 1994 was followed by an increasing trend, which ended with the September 11, 2001 attacks in the USA. There are noticeable clusterings of volatility for the political risk rating. Denmark’s relations with the EU dominated the political scene in the last decade, with the fear that a loss of political independence and national sovereignty would outweigh any economic beneﬁts of the Euro. The decreasing trend from 1992 was due to a rejection by Danish voters of the Maastricht Treaty, which proposed further European integration and a common European defence force. In 1993, the Treaty was approved after certain concessions were granted, and then the political risk started to decrease. However, the political scene changed when early elections were called after September 11, 2001. In the election of November 2001, a right-wing coalition, promising tighter immigration controls, was elected, and replaced the social democrats that had led coalition governments for most of the previous 50 years. The rating stopped falling by late 2001 and remained ﬂat thereafter, as the government proposed new measures to reduce the immigration controversy. As a weighted average of the three risk ratings, the trend and volatility of the composite risk rating reﬂect those of the economic, ﬁnancial and political risk ratings.

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The four risk ratings for Finland are given in Figure 4.104. Finland has an industrial economy largely based on forestry, capital investments and technology. Traditionally, Finland has been a net importer of capital to ﬁnance industrial growth, which has been among the fastest in the EU. Economic risk rating followed a slightly downward trend until 1992, with increasing associated volatility, due to the recession triggered by the collapse of the Soviet market. From 1992 to 2001, the economic rating followed an upward trend associated with increasing volatility, after which it fell and remained ﬂat. Economic risk fell after 1992 as an export-oriented economy recovered from the recession. Since 1994, the GDP growth rate remained high due to the booming Nokia-led electronics industry, and unemployment decreased signiﬁcantly. EU membership in January 1995 resulted in structural changes in key economic sectors. The falling economic rating in 2001 was due to the reduction in worldwide consumer demand for domestic exports. Finland’s ﬁnancial risk rating had a structural change in 1997, prior to which the rating had no trend and low volatility. After 1997, a generally downward trend to late 2000 was followed by a generally upward trend, associated with increasing and decreasing volatility, respectively. As a net importer of foreign capital, Finland was more risky ﬁnancially after 1997. As for the economic risk rating, the political risk rating had a downward trend until 1992 with increasing volatility, followed by an upward trend with decreasing and then increasing volatility. Finns enjoy individual and political freedom, with few tensions between the Finnish-speaking majority and the Swedishspeaking minority. Finland has a mixed presidential/parliamentary system, with executive powers divided between the president and prime minister. Constitutional changes in the late 1980s strengthened the role of the prime minister at the expense of the president. Political risk decreased after 1992, the accession to the EU in 1995 blurred the lines between foreign and domestic policies, and the roles of the president and prime minister have evolved. For the overall country risk, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings. Figure 4.105 presents the four risk ratings for France, the world’s fourth largest economy. The French economy is exceptionally diversiﬁed, with the services sector accounting for a large share of economic activity and responsible for almost all job creation in recent years. Until 1990, the economic risk rating had a downward trend associated with increasing volatility. The pattern changed in 1990 when the rating started an upward trend with a noticeable clustering of volatility. During this period, the government promoted investment and domestic growth in a stable ﬁscal and monetary environment, with the double-digit unemployment rate successfully reduced in the late 1990s. Although the role of government

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has declined in the last 15 years following a wave of privatization, it continued to play a leading role in the provision of services. Financial risk rating in France was high, with no trend and little volatility prior to the 1997 structural change. After 1997, the rating fell and became more volatile with a noticeable peak in 2000, but displayed no trend. Political risk rating had no trend and was volatile throughout the sample period. The centre-right victory in the 1986 led to a left-wing president, Francois Mitterrand, and a right-wing prime minister, Jacques Chirac. As a result, the political risk rating ended a downward trend, started to increase and continued to rise when Mitterrand was re-elected president in 1988. Approval of the Maastricht Treaty in September 1992 led to a fall in the political rating until late 1994. When Chirac became president in May 1995 after a campaign against high unemployment rates, the rating increased in the ﬁrst half of 1995 but decreased substantially in the second half, with an associated peak in volatility. France attracted international condemnation by conducting a series of nuclear tests in the Paciﬁc. In late 1995, France experienced severe labour unrest and protests against government cutbacks. The political rating increased, with a volatility peak in early 1997 when Chirac called early elections. However, the rating fell in 1997 when Lionel Jospin, the Socialist Party leader, became prime minister. France remained politically volatile until 2002. As with the political risk rating, the composite risk rating had no trend and was highly volatile throughout the sample period. The four risk ratings for Germany are given in Figure 4.106. Germany is the world’s third largest economy and Europe’s largest, with exports being the key to economic growth. Until the fall of the Berlin Wall in November 1989, the two countries were known as the German Democratic Republic and Federal Republic of Germany, and thereafter as the Federal Republic of Germany. From January 1985 to September 1990, the risk ratings were available for two countries, after which they were reported for one. Structural changes occurred in the economic, ﬁnancial and composite risk ratings in 1990, reﬂecting the reuniﬁcation. The economic risk rating had a downward trend with increasing volatility until 1990 as the two economies slowed down. After reuniﬁcation, the rating rose by more than 20 points and continued a slight upward trend with low volatility, even though Germany experienced low growth rates and high unemployment. The best performance was in 2000 when the rating reached the high 80s as a result of high annual growth. Government plays a major part in social services and owns some segments of the economy, but with a reduced role after privatization. The economy has been vulnerable to external shocks and domestic structural problems, and reuniﬁcation has proved difﬁcult. However, living standards have risen through improvements in the market

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economy and infrastructure. Despite structural problems, the economy has remained strong and internationally competitive. The ﬁnancial risk rating was ﬂat in the high 80s until 1990, but increased with higher volatility. A second structural change in 1997 decreased the rating by more than 10 points, and became ﬂat but volatile because of the vulnerability to external shocks. The political risk rating had a generally upward trend until 1991, with rising volatility, following improved relations between the two countries in the 1980s, and the events that led to reuniﬁcation. After the 1990 elections, Helmut Kohl became Chancellor and Berlin was named the new capital in 1991. The political rating fell in 1992 given the signiﬁcant political changes. However, it started to rise in 1993 as Germany joined the EU Maastricht Treaty, and Russian and Allied troops ﬁnally left Berlin. The upward trend persisted until 2002 with greater volatility, despite serious domestic political issues. At the 1998 elections, Kohl was replaced by Gerhard Schroeder. When ofﬁcial ﬁgures showed that racist attacks had risen by 40% in 2000, the government sought legal moves in to ban the farright National Democratic Party. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility associated with the economic, ﬁnancial and political risk ratings. Figure 4.107 shows the four risk ratings for Greece. With a small open economy, its industrial base is relatively small compared with the other EU countries. Economic risk for Greece fell during the sample period, leading to a rising economic risk rating from 1984. The rating was quite volatile until 1992, after which the volatility followed an increasing trend. Historically, the government has played a major but ineffective role in industry. In 1998, a privatization wave led the reforms for joining the economic and monetary European Union. Budget deﬁcits and inﬂation were successfully reduced, but poor infrastructure in northern Greece and in the islands slowed the economic decentralization. Financial market reforms occurred during the 1990s. Consequently, ﬁnancial risk decreased until 1997 as the rating increased, with associated clustering of volatility. The rating fell in 1997, after which it was ﬂat but became more volatile due to the susceptibility of Greek ﬁnancial services to global market conditions. Since 1981, the country was led by the Panhellenic Socialist Movement (Pasok), apart from 1990 to 1993 when the New Democracy was in power. Pasok tried to evolve to a mainstream European social democratic party and embraced deeper integration with the EU. The political risk rating had an increasing trend from 1990 to 1998, with no general trend prior to and after this period, and a noticeable clustering of volatility. Greek political risk has been affected by a number of foreign policy issues. The refusal to recognize the Former Yugoslav Republic of Macedonia (FYROM) as the Republic of Macedonia has been a sensitive

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political topic since 1992 but, after 1995, the two major parties commenced negotiations under the auspices of the UN. Perhaps the biggest political concern is the relationship between Greece and Turkey, with Cyprus as a signiﬁcant source of tension in their relationship since the 1950s. In 1996, the dispute over an Aegean islet almost led to armed confrontation between Greece and Turkey. The tension remained high, as both Cyprus and Turkey wanted to join EU, with the conﬂict in Cyprus remaining unresolved. Finally, as a weighted average of the three individual ratings and volatility, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. The four risk ratings for Iceland are given in Figure 4.108. The economy is prone to inﬂation and is export driven, with marine products accounting for the majority of exports, followed by aluminium, ferrosilicon alloys, ﬁshing and ﬁsh-processing equipment, pharmaceuticals and woollen goods. Most exports are to EU and EFTA countries, USA and Japan. A liberal trading policy was strengthened by accession to the European Economic Area in 1994 and to the Uruguay Round agreement. The economic risk rating fell until 1986, increased from 1987, and followed a downward trend until 1990, with associated clustering of volatility. An unstable trend arose due to variations in inﬂation after the oil shocks in the 1970s. Inﬂation was 59% in 1980, fell to 15% in 1987, and rose to 30% in 1988. However, the economic rating had an upward trend after 1990 due to economic reforms and deregulation, so that inﬂation fell dramatically, averaging only 4.85% from 1990 to 2000. With strong growth in GDP, the economic performance excelled in the 1990s, but the rating started to fall in late 1999 as the economy faltered and inﬂation escalated. The economy fell into recession in late 2001, leading to the adoption by the Central Bank of an inﬂation-target exchange rate policy, after which the economic rating remained low with low volatility. There was no trend in the ﬁnancial risk rating prior to 1997, with little or no volatility. The rating fell in 1997 and continued a downward trend until 2001, with increasing volatility, as inﬂation rose and the Krona depreciated. After 2001, the Central Bank policy reforms, which controlled inﬂation and the exchange rate, led to an increasing ﬁnancial rating with falling volatility. Iceland became a republic in 1944, with the president having a similar limited role to that of a monarch in a parliamentary system. The political risk rating fell from 1984 to 1995, when new elections were held. Subsequently, the rating followed an upward trend to the end of the sample, with a noticeable clustering of volatility. Iceland has increased greatly its international proﬁle since the early 1990s with the end of the Cold War, and opened a number of overseas missions. Overall,

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the composite risk rating for Iceland reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.109 presents the four risk ratings for Ireland where the economy is relatively small but closely integrated with the European and global economies. There is a noticeable structural change in the economic risk rating in 1996. Before 1996, the risk rating had no trend and varied slightly around 75, but rose thereafter and remained in the low 90s amid high volatility. In the early 1980s, Ireland faced rising debt and unemployment, and three elections were held over a short period as governments struggled to overcome the economic difﬁculties. The economic situation changed as the volume of Irish exports rose dramatically from 1996 to 2001, leading to average GDP growth of 9.9%. Capital formation grew strongly, in line with heavy investment by business and the growing public capital expenditure programs. US investment was particularly important for the Irish economy, providing new technology, export capabilities and employment opportunities. However, the economic risk rating fell in mid-2001 as the economy started to contract, but this had increased by 2002. There was a generally upward trend until 1997 for the ﬁnancial risk rating, with little or no volatility. The rating fell by more than 10 points in 1997, after which there was no trend but greater volatility. Until 1992, the political risk rating had a decreasing trend, with increasing volatility and a peak in 1992. The rating had a generally upward trend to 2002, with a noticeable clustering of volatility. Mary Robinson of the Labour Party became president in 1990, and the 1992 election brought the Labour Party to power over the two major parties, Fianna Fail and Fine Gael, which represented a break from traditional politics. Labour held the balance of power and formed a coalition with Fianna Fail. When the coalition collapsed in late 1994, Labour formed a new coalition with Fine Gael and the Democratic Left. The political situation changed when Labour lost in 1997 and a new government was formed. The 2002 election returned Fianna Fail and its coalition partner to power. Establishing peace in Northern Ireland has remained a leading political issue for all Irish governments. The composite risk rating most closely reﬂects the trends and volatility in the political risk rating, followed by the ﬁnancial and economic risk ratings. Figure 4.110 presents the four risk ratings for Italy. After WWII, the country has been characterized by a weak political structure but a strong economic base. Italy is currently the world’s ﬁfth largest industrial economy. The political scene underwent a major shift in the 1990s when the ‘Clean Hands’ operation exposed corruption at the highest levels in politics and business. Several former prime ministers were implicated, and many business leaders and politicians were also investigated.

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The downward trend in the economic risk rating prior to 1992 was followed by an upward trend until 2001, with a discernable clustering of volatility. Since 1992, economic policy has focused on reducing taxes, lowering unemployment, which is concentrated in the country’s south, enhancing competitiveness, and reducing both the government budget deﬁcit and national debt. The economic risk rating fell in 2001 due to the downturn in the global economy, and remained ﬂat thereafter. Although there was little variation in the ﬁnancial risk rating prior to 1997, it varied more subsequently but with no trend. There was no structural change in the rating for Italy, but there was a peak in volatility in 2000. The accelerating national debt caused a sharp fall in the rating toward the end of 2000, after which it increased as a result of the adoption of the EU-imposed targets, but it exhibited a decreasing trend after 2001. In the case of the political risk rating, there was a decreasing trend until 1993, with associated increasing volatility owing to the lingering political and economic problems. National referenda in 1993 approved substantial political, economic and ethical reforms, thereby leading to a higher rating, which continued to rise as new political forces and alignments emerged at the 1994 elections. A decreasing trend started in 1998 when Romano Prodi lost a vote of conﬁdence in parliament, and a new government was formed by Massimo D’Alema. Subsequently, the rating increased at the 2000 regional elections, when D’Alema resigned and was replaced by Giuliano Amato. The rating remained ﬂat in the early part of 2001, which saw Berlusconi’s return to power, but started to decrease after the tragic events of September 11, 2001. There was a discernable clustering of volatility in each of the three component risk ratings, with the trend and volatility in the composite risk rating reﬂecting those in the economic and political risk ratings. The four risk ratings in Figure 4.111 are for Luxembourg, which has an extremely high level of economic prosperity. Pastoral land co-exists with a highly industrialized and export-intensive economy. The iron and steel industry is the most important single sector of the economy, and prosperity was originally dependent on steel manufacturing. As the importance of the steel industry declined, diversiﬁcation led to the emergence of a ﬁnancial centre best known for its tax haven status and large banking sector. A tradition of strict banking secrecy contributed to the growth of the ﬁnancial sector and its exploitation for tax evasion and fraud. Government policies promoted its development as an audiovisual and communications centre, and attracted new foreign investments in medium, light and high-tech industries. There was a generally increasing trend for the economic rating throughout the sample, varying around the low 90s after 1997, with the associated volatility falling until 1994, and rising thereafter. For the ﬁnancial rating, there was low risk until 1997, when the rating fell by

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almost 10 points. The ﬁnancial rating increased from 1984 to 1988 with little volatility, and remained at a level of 98 until 1997. There was an increase in volatility for the ﬁnancial risk rating after 1997, with a peak in 2000. A generally falling rating until 2000 followed an upward trend to 2002. However, ﬁnancial risk increased after 1997, with the rating varying around the low to mid-80s. As politics are characterized by stability and long-serving administrations, the political risk rating showed little or no volatility during the sample, with a generally downward trend to 1996 followed by a generally upward trend to 2002. The government is parliamentary in nature, alongside a constitutional monarchy by inheritance. Political stability, excellent communications, easy access to other European centres of inﬂuence and skilled multilingual staff have all contributed to its emergence as a ﬁnancial centre. The country has long been a prominent supporter of European political and economic integration. As an overall measure of country risk for Luxembourg, the composite risk rating followed a generally rising trend, reﬂecting the trends and volatility inherent in the other three risk ratings. Figure 4.112 presents the four risk ratings, risk returns and their associated volatilities for Malta. With a small domestic market, Malta’s economic development is based on tourism promotion and exports of manufactured goods. Malta’s population is tripled by tourist arrivals every year. Since the early 1990s, expansion in these two sectors has been the main engine for strong economic growth. Malta is privatizing statecontrolled ﬁrms and liberalizing markets in order to prepare for membership of the European Union in 2004. However, the island remains politically divided over the question of joining the EU. Moreover, the slowdown in the global economy is holding back exports and tourism. Prior to 1997, the economic risk ratings varied around the mid-80s, with little associated volatility. The rating fell by almost 20 points after 1997, with a volatility peak, increased until early 2000, and followed a generally falling trend to the mid-60s in 2002. Unlike the economic risk rating, the ﬁnancial risk rating had a generally rising trend to 1997, after which the rating fell by almost 15 points and followed a slight declining trend to the mid-60s in 2002. There was little or no volatility over the sample, with two peaks in 1991 and 1997. The ﬁscal situation remains difﬁcult despite some progress in consolidating public ﬁnances. In 1998 the budget deﬁcit reached a level of 10% of GDP. The deﬁcit decreased in 2002, primarily through higher tax rates and improved tax collection. Current expenditures, which were reduced in the late 1990s, increased in 2002 due to the public sector wage bill and subsidies to public enterprises. Formally acquired by Great Britain in 1814, the island strongly supported the UK through both World Wars and remained in the Commonwealth until independence in

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 205

1964. Malta became a republic one decade later. Prior to 1991, the political risk rating was ﬂat in the low 50s, after which the rating followed a generally rising trend reaching the high 80s in 2002. There was little or no volatility over the sample period, with two peaks in 1991. Two parties dominate Malta’s politics, namely the Nationalist Party and the Malta Labour Party. Eddie Fenech Adami’s Nationalist Party returned to power in 1998 and revived the application to enter the EU. Overall, the composite risk rating closely reﬂects the trends and volatility in the ﬁnancial and political risk ratings. Figure 4.113 gives the four risk ratings for the Netherlands. Given the geographical position, the small domestic market and the scarcity of natural resources and raw materials, the Dutch economy is widely regarded as one of the most open in the world. Foreign trade is the key to economic prosperity, with total exports and imports amounting to more than 100% of nominal GDP. Favourable tax policies have placed the Netherlands, together with Ireland and the UK, as European leaders in attracting foreign direct investment. Despite strong growth and low unemployment, rising public debt led to a falling economic rating until 1993, with little volatility apart from two peaks in 1992 and 1993. The rating had a generally rising trend to 1998, with higher volatility, as the government successfully achieved a balance between reduction in public spending and lower taxes. Economic performance was negatively affected by a crisis in emerging markets and a slowdown in the EU in 1998 – 1999, resulting in a falling economic rating. Subsequently, the rating rose but followed a downward trend after 2000 due to a contraction in global trade. The ﬁnancial risk rating was high in the low to mid-90s, with little volatility until 1997, when a structural change caused it to fall by almost 20 points. Financial risk rose after 1997, with the rating varying between 70 and 80, with greater volatility. There was a downward trend in the political risk rating, with some associated volatility until 1995, followed by an upward trend and greater volatility until 2000. Since WWII, the Dutch have been active participants in international affairs and, after 1995, foreign policy focused on promoting trans-Atlantic and European integration, Third World development, and respect for international law, human rights and democracy. Owing to the emphasis on consensus in Dutch politics, elections do not lead to drastic changes in foreign and domestic policy. Political risk rose in early 2001 when the government resigned, following internal criticism for failing to stop the Srebrenica massacre in 1995 and the shooting of anti-immigration party leader Pim Fortuyn by a lone gunman. As a weighted average of three risk ratings, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings.

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Figure 4.114 presents the four risk ratings for Norway. With a small economy, low population, one of the world’s highest standards of living and a liberal environment for foreign trade, Norway has also built one of the largest and most modern shipping ﬂeets among maritime nations, and is the world’s second largest exporter of oil and gas. Trade patterns are heavily affected by oil output and prices, with offshore oil and gas contributing more than half the total export revenue. In the mid-1970s, the economy prospered with the emergence of major oil and gas production. The economic risk rating had a generally upward trend for 1989 – 1997, with associated volatility, prior to and after which the rating had no trend. Oil production increased signiﬁcantly in the 1990s with the discovery of new offshore ﬁelds, which stimulated the economy. However, the rating fell considerably during 1997– 1999 due to a sharp fall in the trade surplus as oil prices declined. The rating rose when oil prices and trade surplus recovered, with a volatility peak in 2000, after which it varied in the low to mid-90s. Norway experienced low ﬁnancial risk, with a stable exchange rate but a foreign trade balance susceptible to oil prices. The ﬁnancial risk rating had no trend and ranged from the high 80s to mid-90s over the sample, with greater volatility after 1997. Over the last decade, Norway has been an active participant in international politics, mediating between Israel and the Palestine Liberation Organization, and encouraging talks between the Sri Lankan government and Tamil separatists. Norwegians value their independence and prosperity greatly, and rejected EU membership in 1994. The Labour Party dominated Norwegian politics until 2001 when it suffered an historic defeat due to the modernization of the economy that broke traditional links with trade unions. Until 1992 the political risk rating had a generally decreasing trend, with associated volatility, followed by a generally increasing trend with greater volatility. There was a volatility peak in 2000 when the government resigned over proposed gas-ﬁred power plants which would have had a serious impact on climate change. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the economic, ﬁnancial and political risk ratings. Figure 4.115 displays the four risk ratings for Portugal, with the services sector, particularly tourism, playing an increasingly important role. Signiﬁcant progress has been made in raising the living standards, with GDP per capita rising from 51% of the EU average in 1985 to 78% in 2002. Membership of the EU in 1986 contributed to stable economic growth, largely through increased trade ties and inﬂows of funds to improve the country’s infrastructure. A generally upward trend for the economic risk rating to 1997 was followed by a decreasing trend until the end of the sample, with a noticeable clustering of volatility. After

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 207

a recession in 1993, which had only a slight negative impact on the rating, Portugal grew at an average annual rate of 3.3%, well above the EU average. Fiscal deﬁcits were reduced and structural reforms were undertaken to meet the EMU criteria, with Portugal adopting the Euro in 1999. The EMU led to exchange rate stability, and falling inﬂation and interest rates, which lowered the cost of public debt. However, the economic risk rating fell after 1997, with an expansionary ﬁscal policy in 1999 and 2000 leading to a substantial deterioration of the budget deﬁcit and a declining growth rate in late 2001 and 2002. There were two structural changes for the ﬁnancial risk rating in 1988 and 1997. The rating exhibited little volatility and a steep increasing trend to 1988, when it rose by more than 10 points, followed by no trend in the high 80s until 1997, when the rating fell by almost 15 points. Financial risk increased after 1997, with the rating having a generally downward trend to 2002, as reforms on ﬁscal austerity, productivity growth, and reduction of chronic external debt yet to be implemented. Portugal moved from an authoritarian rule to parliamentary democracy following a bloodless military coup in 1974, joined the EU in 1986, and has since moved toward greater political integration with Europe. In 1999, Portugal returned its last overseas territory, Macau, to China, ending an era as a colonial power. An absence of trend for the political risk rating to 1991 was followed by an upward trend to 1997, where the rating varied around 90, before falling in late 2001 and rising in 2002, with tri-modal associated volatility. As a weighted average of the three risk ratings, the composite risk rating reﬂects the trends and volatility of the economic, ﬁnancial and political risk ratings. The four risk ratings for Spain are presented in Figure 4.116. After the death of General Franco in 1975, Spain embarked on a transition to democracy under King Juan Carlos. Accession to the EU in 1986 required an open and revised economy, modernized industrial base and improved infrastructure. Generally, there were similar positive trends for the economic, political and composite risk ratings, with discernable volatility during the sample. The ﬁnancial risk rating exhibited no trend, with greater volatility and higher risk after 1997. Economic growth was strong in the late 1980s, but investment and private consumption declined and unemployment rose in 1993. Four devaluations of the Peseta after 1992 led to competitive exports, including tourism, resulting in a modest exportled recovery in 1994, with higher economic growth and investment and lower unemployment, inﬂation, and public debt to GDP ratio. Consolidation of the recovery during the ﬁrst Aznar administration led to a rise in the economic risk rating in 1996, after which it remained high due to increased consumer conﬁdence and higher domestic private consumption. The economic and ﬁnancial ratings were in the high 70s after 2001, reﬂecting

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fundamental challenges to reduce public sector deﬁcit and unemployment, reform law and investment regulations, decrease inﬂation and raise GDP per capita. Although the political risk rating was generally rising, it exhibited falling trends in 1989 –1992, 1992– 1995 and 1998 – 2000, reﬂecting an unstable political environment. Spain became a parliamentary monarchy in 1978, and joined NATO and EU under the 1982 Socialist government of Felipe Gonzales, and EMU under the 1996 Popular Party government of Jose Maria Aznar. Serious domestic tensions remain in the Northern Basque region with the long-running campaign by the Basque Fatherland and Liberty (ETA), a separatist organization founded in 1959. ETA is believed responsible for the deaths of more than 800 people since a terrorist campaign began in 1968. In recent years, the government has had greater success in controlling ETA due to increased security cooperation with France. Spain has become a major participant in multilateral international security activities in NATO. Overall, the composite risk rating reﬂects the trends and volatility in the three components. Figure 4.117 presents the four risk ratings for Sweden, a leading manufacturer and exporter, with an advanced high-tech sector and rich natural resources. The ‘Swedish model’ represents a mixed economy of public– private partnership, centralized wage negotiation, and a heavily tax-subsidized social security network. Until 1991 the economic risk rating showed a downward trend, followed by a rising trend to 2001 and no trend to 2002, with a clustering of volatility. Low unemployment in the 1980s led to higher inﬂation and a lower economic risk rating, showing that low unemployment rates were unsustainable. Unemployment rose substantially during the severe recession of 1991– 1993, but fell as the economy recovered after the austerity measures of the mid-1990s, leading to high growth, low inﬂation, booming exports, ﬁscal consolidation, pensions reform and falling public debt. The Internet revolution, which created a new generation of entrepreneurs in the late 1990s, had a positive impact and made Stockholm one of the leading centres of information technology. However, the high-tech sector faltered after 2000, and the rating fell by 10 points in 2001, but remained at 82 until 2002. The ﬁnancial risk rating rose to the low 90s by 1987, was entirely ﬂat until 1992, had a generally decreasing trend to 1999, after which it rose but followed no trend to 2002, and had virtually no volatility prior to 1997, but with two peaks in 1997 and 1999. Sweden became ﬁnancially riskier after the crisis in the early 1990s, but the austerity measures in the mid-1990s had no impact on the rating. With the Social Democratic party in power for much of the last 70 years, the political risk rating had a generally declining trend to 1992, followed by an increasing trend, especially after the economic reforms, with a noticeable clustering of volatility. Since the end of the Cold War,

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 209

Sweden joined the EU in 1995, remained outside the EMU in 1999, and did not join NATO owing to its policy of neutrality and non-participation in military alliances. Sweden is active in the UN and international peacekeeping efforts, and has close economic, social and political ties with its Nordic neighbours. The composite risk rating had a downward trend until 1996, followed by an upward trend as the economy recovered, and reﬂected the volatility in the three component risk ratings. Figure 4.118 presents the four risk ratings for Switzerland, a small open economy with perhaps the world’s highest per capita income and wages. With trade underlying economic prosperity, the country has liberal trade and investment policies, a conservative ﬁscal policy and a very strong currency. Lacking natural resources, economic prosperity also depends on skilled labour, technological expertise in manufacturing, and services, such as tourism, banking, engineering and insurance. The Swiss economy stagnated from 1991 to 1997, becoming the weakest in Western Europe, with a zero average annual GDP growth rate. The economic risk rating varied around the 90s until 1987, with little volatility, fell to 1992, with a volatility peak, rose with mild volatility until 1994, and had no trend to 1997, with greater volatility and a peak in 1996. A strong economic recovery after mid-1997 led to rising growth and a higher rating to 2000. However, as economic growth slowed due to contractions in the EU and USA, the rating fell after 2000 with low associated volatility, improving slightly to the high 80s by the end of 2002. Given the strong protective investment policies and high standards in the banking and ﬁnancial services, Switzerland was risk free until 1996, as the ﬁnancial risk rating rose from 95 to 100 in 1984 until late 1996, after which it followed a falling trend to late 2000 and an upward trend until 2002, with high associated volatility. Switzerland has a diverse society, a stable democratic government, and minor domestic policy issues. The political risk rating fell during 1984 – 1994, had no trend to 2000, and followed a generally rising trend thereafter, with little volatility apart from some volatility peaks. Although the country was not involved in WWI or WWII, Swiss banks in the 1990s were pressured to return deposited funds to the relatives of Holocaust victims. The changing international climate has led to a revision in the defence, neutrality and immigration policies. In recent years the Swiss have broadened the scope of their activities without compromising their neutrality, and voted in favour of joining the UN in 2002. Unlike the other three risk ratings, the composite risk rating had greater volatility, with a falling trend to 1999 followed by an upward trend to 2002. Figure 4.119 presents the four risk ratings for Turkey, whose economy is a combination of modern industry and commerce, large agricultural

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sector, growing private sector, and major state involvement in industry, banking, transport and communications. Textiles and clothing are the major exports, especially to the EU. The economic risk rating was at around 55, with no trend but mild volatility, apart from two peaks in 2001, reﬂecting unstable growth. Reforms in the 1980s resulted in strong growth, but the expansion was interrupted by crises in 1994, 1999 and 2001 due to massive rises in public debt, high inﬂation, low foreign investment, weak banking sector and growing trade deﬁcits. The 1999 IMF reforms had collapsed by late 2000, followed by a crisis in early 2001, leading to a ﬂoating of the Lira, a fall of 27 points then an increase of 19 in the rating in 2002 as large IMF loans and economic reforms resulted in lower interest rates and inﬂation, and a stable Lira. There was a structural change in the ﬁnancial risk rating in 1992 when it rose by 34 points, following declining trends around 1992, with ﬂuctuating volatility. Financial distress after 1994 led to a fall in the rating, which rose by 2002 due to IMF supported structural reforms. Once the centre of the Ottoman Empire, Turkey was founded as a republic in 1923 under Kemal Ataturk, joined the UN in 1945 and NATO in 1952, and became a secular democratic state in 1982. Constitutional changes after 1999 strengthened individual human rights. A structural change in the political risk rating in 1990 – 1991, when the USled coalition against Iraq launched air strikes from Turkish bases, led to an increase of 29 points and two volatility peaks. The rating had greater volatility after 1988, falling trends to 1990 and from 1991 to 1998, and rose after 1998 due to a calmer political environment. Turkey occupied northern Cyprus in 1974 to prevent a Greek takeover and recognized the Turkish Republic of Northern Cyprus in 1984. Tensions with Greece have recently eased, with both Turkey and Cyprus seeking EU membership. The Kurdish issue led to a civil war after 1984 between Turkish troops and separatist forces of the Kurdistan Workers’ Party (PKK), followed by a unilateral ceaseﬁre in 1999. The composite risk rating essentially reﬂects the trends and volatility in the political risk rating. The four risk ratings for the UK in Figure 4.120 reﬂect one of the world’s largest economies, and an important member of EU, UN and NATO. Britain’s imperial power declined after WWII and, given the continuing links with the former colonial territories, close ties with the USA, and a separate sense of identity, membership with the EU was delayed until 1973. Economic prosperity, formerly based on manufacturing, now depends on the services sector, particularly banking, insurance and ﬁnance. Membership in the EMU is still under debate, with a referendum proposed only if joining the EMU can be shown to improve investment, employment and growth. The UK experienced two deep recessions in the early 1980s and 1990s, and a severe crisis for the beef

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 211

industry in 1996 with ‘mad cow’ disease. The economic risk rating showed no trend and little volatility until 1997, when the newly elected Labour government, emphasizing the need for sound economic management, embarked on structural reforms. After 1997, the rating rose by almost 20 points to the low 80s, with greater volatility. Lower growth in 2001– 2002 was due to the global downturn, high value of the Pound, lower manufacturing and reduced exports, but the economy remained strong with low inﬂation, interest rates and unemployment. There was no trend in the ﬁnancial risk rating, ranging from the high 80s to 100 until 1997, when it fell by almost 20 points. It stayed in the low 70s until 2002, with little volatility throughout the sample, apart from a peak in 1997. The UK is a constitutional monarchy, governed by the Conservative Party until 1997 and Tony Blair’s Labour Party thereafter. The political risk rating followed a decreasing trend until 1990, when Margaret Thatcher resigned as prime minister, and an increasing trend to 2002, with a discernable clustering of volatility. Elections in 1997 resulted in a substantial rise in the political risk rating, which was associated with a volatility peak. Signiﬁcant constitutional reforms have been made in recent years, leading to the 1999 establishment of the Scottish Parliament, the National Assembly for Wales and the Northern Ireland Assembly. As an overall measure of country risk, the composite risk rating reﬂects the trends and volatility in the economic and political risk ratings.

4.4. Conclusion This chapter assessed monthly ICRG country risk ratings and risk returns for 120 representative countries by geographic region. A detailed evaluation of ICRG risk ratings and risk returns, where the latter is deﬁned as the monthly percentage change in the respective risk ratings, was provided. For each of the 120 selected countries, the trends and associated volatility of the four country risk ratings and risk returns were analysed according to economic, ﬁnancial and political environments in the country. There were substantial changes in the trends of the risk ratings, as well as in their associated sample volatilities for the 120 countries across the eight geographic regions. Similarly, substantial differences were evident in the risk returns, as well as in their volatilities. This chapter provided, for the ﬁrst time, a comparative assessment of the trends and volatility of country risk ratings for the 120 countries, and highlighted the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating.

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References BBC News (2003), Country Proﬁles and Timeline, http://news.bbc.co.uk/1/shared/bsp/hi/ country_proﬁles/html/default.stm. BBC News (2004), Country Proﬁles, http://news.bbc.co.uk/1/hi/country_proﬁles/default. stm. Central Intelligence Agency (2003), The World Factbook 2002, http://www.odci.gov/ cia/ publications/factbook/index.html. Central Intelligence Agency (2004), The World Factbook 2004, http://www.cia.gov/cia/ publications/factbook/index.html. International Country Risk Guide (2002), The PRS Group, New York. The Economist (2003), Country Brieﬁngs, http://www.economist.com/countries/. The PRS Group, Inc. (2003), CountryData Country List, http://www.countrydata.com/help/ countries.phtml. UK Trade and Investment (2004), Country Information, http://www.uktradeinvest.gov.uk. US Department of State (2003), Countries and Regions, http://www.state.gov/countries/. US Department of State (2004), Countries and Regions, http://www.state.gov/countries/.

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 213

Figure 4.1. Risk ratings, risk returns and volatilities for Bangladesh RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

70 60 50

400 300

40

100 0

84 86 88 90 92 94 96 98 00 ECO-R ECO-V

POL-V

POL-R 70 60 50 40 30 20

400 300 200 100 0

300 200

200 100 0

400

FIN-R 80 70 60 50 40 30 20

84 86 88 90 92 94 96 98 00 FIN-R FIN-V

COM-V

COM-R 70 60 50

400

40

300

30

200 100 0

84 86 88 90 92 94 96 98 00 POL-R POL-V

84 86 88 90 92 94 96 98 00 COM-R COM-V

RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

.06 .05 .04 .03 .02 .01 .00

ECO-R .3 .2 .1 .0 −.1 −.2 −.3

FIN-V

.1 .04

.0

.03

−.1

.02

−.2

.01 .00

84 86 88 90 92 94 96 98 00 ECO-R ECO-V

POL-V

POL-R .15

84 86 88 90 92 94 96 98 00 FIN-R FIN-V

COM-V

.10 .05

.020

.00

.016

−.05

.012

−.10

.008 .004 .000

FIN-R .2

.008 .006

COM-R .12 .08 .04 .00 −.04 −.08

.004 .002

84 86 88 90 92 94 96 98 00 POL-R POL-V

.000

84 86 88 90 92 94 96 98 00 COM-R COM-V

Note: Economic (ECO), Financial (FIN), Political (POL) and Composite (COM) risk ratings (risk returns) and their associated volatilities are denoted by R and V, respectively

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Figure 4.2. Risk ratings, risk returns and volatilities for India RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80 75 70 65 60 55 50

160 120 80 40 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 70 60 50 40 30 20

POL-V

600 500 400 300 200 100 0

FIN-V

500 400 300 200 100 0

84

FIN-R 90 80 70 60 50 40

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80 70 60 50 40 30

COM-V

400 300 200 100

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

.020 .016 .012 .008 .004 .000

ECO-R .15 .10 .05 .00 −.05 −.10 −.15

FIN-V

FIN-R

.15 .10 .05 .00 −.05 −.10 −.15

.016 .012 .008 .004

84

86

88

90 92 94 96 98 ECO-R ECO-V

.000

00

POL-V

POL-R .3

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .15 .10

.2 .06 .05 .04 .03 .02 .01 .00

.05

.1 .0

.020

.00

−.1

.015

−.05

−.2

−.10

.010 .005

84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 215

Figure 4.3. Risk ratings, risk returns and volatilities for Pakistan RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 75 70 65 60 55 50

ECO-V

100 80 60 40 20 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 70

POL-V

FIN-R 80 70 60 50 40 30

FIN-V

500 400 300 200 100 0

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 65 60 55 50 45 40 35

COM-V

60 50

500

40

400

30

300

20

200

150 100 50

100 0

200

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.05 .04 .03 .02 .01 .00

ECO-R .3 .2 .1 .0 −.1 −.2

FIN-V

FIN-R

.1 .03

.0 −.1

.02

−.2

.01 84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .2

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .08 .04

.1 .028 .024 .020 .016 .012 .008 .004 .000

.00

.0 −.1 −.2

−.04

.008

−.08

.006 .004 .002

84

86

88

90 92 94 96 98 POL-R POL-V

00

.2

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

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Figure 4.4. Risk ratings, risk returns and volatilities for Sri Lanka RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80

ECO-V

FIN-R 80 70 60 50 40 30

FIN-V

70 60

400 300 200

50

800

40

600 400

100 0

200 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 70 60 50 40 30 20

400 300

90 92 94 96 98 POL-R POL-V

0

00

00

COM-R 70 50 40

300 100

88

90 92 94 96 98 FIN-R FIN-V

400

100 86

88

60

200

84

86

COM-V

200 0

84

30

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2 .1 .0 −.1 −.2 −.3

.08 .06 .04 .02 .00

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R .2

POL-V

FIN-V

FIN-R

.1 .0 .025 .020 .015 .010 .005 .000

−.1 −.2

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .08

COM-V

.04

.1

.00

.0 −.1

.03

−.2

.02 .01 .00

84

86

88

90 92 94 96 98 POL-R POL-V

00

.2

−.04 .006 .005 .004 .003 .002 .001 .000

−.08

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 217

Figure 4.5. Risk ratings, risk returns and volatilities for Australia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 88

FIN-V

FIN-R 100 90

84

80 40 0

76

400

70

72

300

60

68

200 100

84

86

88

90 92 ECO-R

94 96 98 ECO-V

0

00

POL-V

100 80 60 40 20 0

80

80

120

POL-R 92 88 84 80 76 72

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 88 84

40

80

30

76

20

72

10 84

86

88

90 92 POL-R

94 96 98 POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .08 .04 .00 −.04 −.08 −.12

.015 .010

FIN-V

.10 .05 .00 −.05 −.10 −.15 −.20

.04 .03 .02

.005

.01

.000

.00

84

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

POL-R .08 .06 .04 .02 .00 −.02 −.04

.004 .003 .002 .001 .000

FIN-R

84

86

88

90 92 POL-R

94 96 98 POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

.0020 .0016 .0012 .0008 .0004 .0000

84

COM-R .04 .02 .00 −.02 −.04 −.06

86

88

90 92 94 96 98 COM-R COM-V

00

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Figure 4.6. Risk ratings, risk returns and volatilities for Brunei RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 100 96

FIN-V

92

20

100

88

16

80

84

60

80

12

40

98 96 94 92

8

90

4

20 0

FIN-R 100

86

88

90

92

94

ECO-R

96

98

0

00

86

88

90

ECO-V

92

94

FIN-R

POL-V

POL-R 84 82 80 78 76 74 72

20 15 10

96

98

00

FIN-V

COM-V

COM-R 90 88 86 84 82 80

16 12 8

5

4

0

0

86

88

90

92

94

POL-R

96

98

00

86

88

90

POL-V

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .15 .10 .05 .00 −.05 −.10 −.15

.015 .010

.06 .04 .02

.0020

.00

.0016

−.02

.0012

−.04

.0004 86

88

90

92

94

ECO-R

96

98

.0000

00

86

88

90

ECO-V

92

94

FIN-R POL-R .06 .04 .02 .00 −.02 −.04 −.06

POL-V

.003 .002

96

98

00

FIN-V COM-R .04

COM-V

.02 .00 −.02 .0016

−.04

.0012 .0008

.001 .000

FIN-R

.0008

.005 .000

FIN-V

.0004 86

88

90

92

94

POL-R

96

98

POL-V

00

.0000

86

88

90

92

94

COM-R

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 219

Figure 4.7. Risk ratings, risk returns and volatilities for China RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90

ECO-V

FIN-R

FIN-V

80 300 250 200 150 100 50 0

70 60 50

800 600 400 200

86

88

90

92

94

ECO-R

96

98

0

00

86

88

90

ECO-V

92

94

FIN-R POL-R 80

POL-V

96

98

00

FIN-V COM-R 80 75 70 65 60 55

COM-V

75 70 120 100 80 60 40 20 0

100 90 80 70 60 50 40

65 60

150

55

100 50

86

88

90

92

94

POL-R

96

98

0

00

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.2

FIN-R

FIN-V

.16 .12 .08 .04 .00 –.04 –.08

.1 .0 .04

–.1

.03

–.2

.02 .01 .00

86

88

90

92

94

ECO-R

96

98

00

.024 .020 .016 .012 .008 .004 .000

86

88

90

ECO-V

92

94

FIN-R POL-R

POL-V

.08

96

98

00

FIN-V COM-R .08

COM-V

.04

.04

.00 .006 .005 .004 .003 .002 .001 .000

86

88

90

92 POL-R

94

96

98

POL-V

00

–.04 .005 –.08 .004 .003 .002 .001 .000

.00 –.04 –.08

86

88

90

92

COM-R

94

96

98

COM-V

00

220

S. Hoti and M. McAleer

Figure 4.8. Risk ratings, risk returns and volatilities for Hong Kong RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

600 500 400 300 200 100 0

ECO-R 100 90 80 70 60 50

FIN-V

FIN-R

160 120 80 40

84

86

88

90

92

94

ECO-R

96

98

0

00

84

86

88

ECO-V

90

92

94

FIN-R

POL-V

POL-R 90

96

98

COM-V

COM-R 90 85 80 75 70 65 60

70 60

400

50

300 200 100 84

86

88

90

92

94

POL-R

96

98

00

250 200 150 100 50 0

84

00

FIN-V

80

0

95 90 85 80 75 70

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

FIN-V

FIN-R

.2

.16

.0

.12

−.2

.08

−.4

.04 .00

.4

84

86

88

90

92

94

ECO-R

96

98

00

.0025 .0020 .0015 .0010 .0005 .0000

84

86

88

ECO-V

90

92

94

FIN-R

POL-V

POL-R

.12

96

98

00

FIN-V

COM-V

COM-R .12 .08

.08 .012 .010 .008 .006 .004 .002 .000

.04 .00 −.04 −.08

.04 .010 .008

.00 −.04 −.08

.006 .004 .002

84

86

88

90

92

POL-R

94

96 POL-V

98

00

.06 .04 .02 .00 −.02 −.04 −.06

.000

84

86

88

90

92

COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 221

Figure 4.9. Risk ratings, risk returns and volatilities for Indonesia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80 70 60 50 40 30

ECO-V

1000 800 600 400 200 0

84

86

88

90

92

94

ECO-R

96

98

00

1000 800 600 400 200 0

84

86

88

ECO-V

90

92

94

FIN-R POL-R 70

POL-V

280 240 200 160 120 80 40 0

FIN-R 90 80 70 60 50 40 30

FIN-V

96

98

00

FIN-V COM-R 80

COM-V

70

60

60 50

50 40 30

84

86

88

90

92

94

POL-R

96

98

200 100 0

00

40

300

84

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.6

FIN-R

FIN-V

.4 .2

.25

.0

.20

−.2

.15

−.4

.10 .00

.2

.16

.0

.12

−.2

.08

−.4

.04

.05 84

86

88

90

92

94

ECO-R

96

98

.00

00

84

86

88

ECO-V

90

92

94

FIN-R POL-R

POL-V

.2

96

98

00

FIN-V COM-R .2

COM-V

.1

.1

.0 −.1

.04

−.2

.03 .02 .01 .00

84

86

88

90

92

POL-R

94

96 POL-V

98

00

.4

.0 .030 .025 .020 .015 .010 .005 .000

−.1 −.2

84

86

88

90

92

COM-R

94

96

98

COM-V

00

222

S. Hoti and M. McAleer

Figure 4.10. Risk ratings, risk returns and volatilities for Japan RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 96 92 88 84 80 76 72 68

ECO-V

200 150 100 50 0

84

86

88

90 92 ECO-R

94

96 98 ECO-V

00

POL-R 95 90 85 80

POL-V

100 80

75 70

60 40 20 0

FIN-R

FIN-V

100 96

100 80 60 40 20 0

92 88

84

86

88

90 92 FIN-R

94

96 98 FIN-V

00

COM-R 96 92 88 84 80 76

COM-V

80 60 40 20

84

86

88

90 92 POL-R

94

96 98 POL-V

0

00

84

86

88

90 92 COM-R

94

96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .08 .04 .00 −.04 −.08 −.12

ECO-V

.016 .012

.04 .00

.004

−.04

.003

−.08

.002

.008 .004

.001

.000

.000

84

86

88

90 92 ECO-R

94

96 98 ECO-V

00

POL-V

POL-R .08 .06 .04 .02 .00 −.02 −.04

.005 .004 .003 .002 .001 .000

FIN-R .08

FIN-V

84

86

88

90 92 POL-R

94

96 98 POL-V

00

84

86

88

90 92 FIN-R

94

96 98 FIN-V

00

COM-V

.0020 .0016 .0012 .0008 .0004 .0000

84

COM-R .04 .02 .00 −.02 −.04 −.06

86

88

90 92 COM-R

94

96 98 COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 223

Figure 4.11. Risk ratings, risk returns and volatilities for Malaysia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90

FIN-V

FIN-R 100 90 80 70 60 50

80 70

400

60

300

800 600

200

400

100

200

0

84

86

88

90 92 ECO-R

94

96 98 ECO-V

0

00

POL-V

POL-R 85 80 75 70 65 60 55

160 120

40 88

90 92 POL-R

94

96 98 POL-V

0

00

90 92 FIN-R

94 96 98 FIN-V

00

COM-R 85 80 75 70 65 60

120 80

86

88

160

40 84

86

COM-V

80 0

84

84

86

88

90 92 COM-R

94 96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.2 .1 .0 −.1 −.2 −.3

.1 .0 .025 .020 .015 .010 .005 .000

−.1 −.2

.06 .04 .02

84

86

88

90 92 ECO-R

94

96 98 ECO-V

.00

00

POL-V

.0028 .0024 .0020 .0016 .0012 .0008 .0004 .0000

.08

POL-R .08

84

86

88

90 92 FIN-R

94 96 98 FIN-V

00

COM-V

COM-R .04

.04

.00

.00

−.04

−.04

.008

−.08

.006

−.08 −.12

.004 .002 84

86

88

90 92 POL-R

94

96 98 POL-V

00

.000

84

86

88

90 92 COM-R

94 96 98 COM-V

00

224

S. Hoti and M. McAleer

Figure 4.12. Risk ratings, risk returns and volatilities for Mongolia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 70

FIN-V

FIN-R 80 75 70 65 60 55 50

60 500

50

400

40

150

30

100

300 200

50

100 0

86

88

90

92 94 ECO-R

96 98 ECO-V

POL-V

POL-R 80

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R 72 68 64 60 56 52

75

160

70

120

65

80

60

40 0

0

00

55 86

88

90

92 94 POL-R

96 98 POL-V

00

100 80 60 40 20 0

86

88

90

92 94 COM-R

96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .6

ECO-V

FIN-R

FIN-V

.0

.20

−.2

.15

−.4

.10 .05 .00

.1

.2

.25

86

88

90

92 94 ECO-R

96 98 ECO-V

00

POL-V

POL-R .15

.06 .05 .04 .03 .02 .01 .00

.0 −.1 −.2

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R .12 .08

.10

.04

.05 .020

.00

.010

.016

−.05

.008

.012

−.10

.006

.008

.004

.004

.002

.000

86

88

90

92 94 POL-R

96 98 POL-V

00

.3 .2

.4

.000

.00 −.04 −.08

86

88

90

92 94 COM-R

96 98 COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 225

Figure 4.13. Risk ratings, risk returns and volatilities for New Zealand RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90 85 80 75 70 65 60

160 120 80 40 0

84

86

88

90 92 ECO-R

94

96 98 ECO-V

00

POL-V

POL-R 92

FIN-V

1000 800 600 400 200 0

84

FIN-R 100 90 80 70 60 50

86

88

90 92 FIN-R

94 96 98 FIN-V

00

COM-V

COM-R 88

88

84

84 80 60

50

76

40 30

40

76 72

20

20 0

80

80

10 84

86

88

90 92 POL-R

94

96 98 POL-V

0

00

84

86

88

90 92 COM-R

94 96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.2 .1 .0 −.1 −.2 −.3

.1 .0 .025 .020 .015 .010 .005 .000

−.1 −.2

.06 .04 .02

84

86

88

90 92 ECO-R

94

96 98 ECO-V

.00

00

POL-V

.0025 .0020 .0015 .0010 .0005 .0000

.08

POL-R .04 .02 .00 −.02 −.04 −.06

84

86

88

90 92 FIN-R

94 96 98 FIN-V

00

COM-V

COM-R .04 .02 .00 −.02 −.04 −.06

.003 .002 .001

84

86

88

90 92 POL-R

94

96 98 POL-V

00

.000

84

86

88

90 92 COM-R

94 96 98 COM-V

00

226

S. Hoti and M. McAleer

Figure 4.14. Risk ratings, risk returns and volatilities for North Korea RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 60 50 40

600 500 400 300 200 100 0

30 20 10

86

88

90

92 94 96 98 ECO-R ECO-V

POL-R 70 65 60

120 100 80 60 40 20 0

55 50 45

86

88

90

92 94 96 98 POL-R POL-V

50

300

40

200

30

100

20

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R 65

300

60

250

55

200

50

150

45

100

40

50

35

0

00

FIN-R 60

400

0

00

POL-V

FIN-V

86

88

90

92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 1.2

FIN-V

FIN-R

.3 .2 .1 .0 −.1 −.2 −.3

0.8 0.4 0.0

1.2

−0.4

0.8 0.4 0.0

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-V

.020 .016 .012 .008 .004 .000

86

POL-R

.10 .05 .00 −.05 −.10 −.15

88

90

92 94 96 98 POL-R POL-V

00

.06 .05 .04 .03 .02 .01 .00

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .2

.04

.1

.03

.0

.02

−.1

.01

−.2

.00

86

88

90

92 94 COM-R

96 98 COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 227

Figure 4.15. Risk ratings, risk returns and volatilities for Papua New Guinea RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 80 75 70 65 60 55 50

70 60 500 400 300 200 100 0

50 40

84

86

88

90

92

94

ECO-R

96

98

00

200 160 120 80 40 0

84

86

88

ECO-V

90

92

94

FIN-R

POL-V

POL-R 70

96

98

00

FIN-V

COM-V

COM-R 72 68 64 60 56 52 48

65 60 100 80 60 40 20 0

55 50

84

86

88

90

92

94

POL-R

96

98

00

200 160 120 80 40 0

84

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .2 .1 .0 −.1 −.2 −.3 −.4

ECO-V

.16 .12

.012 .008

.04

.004 84

86

88

90

92

94

ECO-R

96

98

POL-R

.08 .04 .00 −.04 −.08 −.12

.008 .004 86

88

90

92

POL-R

86

88

90

94

96

98

POL-V

92

94

FIN-R

.012

84

84

ECO-V

POL-V

.000

.000

00

.12 .08 .04 .00 −.04 −.08 −.12

.016

.08 .00

FIN-R

FIN-V

00

96

98

00

FIN-V

COM-V

.010 .008 .006 .004 .002 .000

84

86

COM-R .08 .04 .00 −.04 −.08 −.12

88

90

92

COM-R

94

96

98

COM-V

00

228

S. Hoti and M. McAleer

Figure 4.16. Risk ratings, risk returns and volatilities for the Philippines RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 80 70 60 50 40 30 20

70 60

400 300 200

50

800

40

600 400

100 0

200 84

86

88

90

92

94

ECO-R

96

98

84

86

88

ECO-V

90

92

94

FIN-R POL-R 80 70 60 50 40 30

POL-V

500 400 300 200 100 0

0

00

96

98

00

FIN-V COM-R 80 70 60 50 40 30

COM-V

400 300 200 100

84

86

88

90

92

94

POL-R

96

98

0

00

84

86

88

90

92

94

COM-R

POL-V

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.025 .020 .015 .010 .005 .000

ECO-V

.15 .10 .05 .00 −.05 −.10 −.15

.2 .1 .0 −.1

.04

−.2

.03 .02 .01

84

86

88

90

92

94

ECO-R

96

98

.00

00

84

86

88

ECO-V

90

92

94

FIN-R POL-R

POL-V

.05 .04

.3 .2

96

98

00

FIN-V COM-R .15

COM-V

.10

.1 .0

.020

−.1

.015

−.05

.010

−.10

−.2

.03 .02

.05 .00

.005

.01 .00

FIN-R

FIN-V

84

86

88

90

92

POL-R

94

96

98

POL-V

00

.000

84

86

88

90

92

COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 229

Figure 4.17. Risk ratings, risk returns and volatilities for Singapore RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 100

80

200 150

70

100 50 84

86

88

90

92

94

ECO-R

96

98

00

POL-R 92 88 84 80

60

76 72

40 20 86

88

90

92

84

86

88

94

POL-R

96

98

90

92

FIN-R

80

84

200 160 120 80 40 0

ECO-V

POL-V

0

FIN-R 100 95 90 85 80 75

90

250

0

FIN-V

96

98

00

FIN-V

COM-V

COM-R 96 92 88 84 80 76

80 60 40 20 0

00

94

84

86

88

POL-V

90

92

COM-R

94

96

98

COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

−.1

.03

−.2

.02 .01 .00

84

86

88

90

92

ECO-R

94

96

98

00

.005 .004 .003 .002 .001 .000

−.04 −.08

84

86

88

90

92

FIN-R

ECO-V

POL-V

.0024 .0020 .0016 .0012 .0008 .0004 .0000

.00

.0

.04

POL-R

.06 .04 .02 .00 −.02 −.04

94

96

98

00

FIN-V

COM-V

COM-R .04 .02 .00 −.02

.0012

−.04

.0008 .0004

84

86

88

90

92

POL-R

94

96

POL-V

98

00

.08 .04

.1

.0000

84

86

88

90

92

COM-R

94

96

98

COM-V

00

230

S. Hoti and M. McAleer

Figure 4.18. Risk ratings, risk returns and volatilities for South Korea RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90

FIN-V

FIN-R 100 90 80 70 60 50

80 400

70

300

60

800

200

50

600

100 0

200 86

88

90

92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 85 80 75 70 65 60 55

150 100

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 85 80 75 70 65 60

200 150 100

50 0

400

50 86

88

90

92 94 POL-R

96 98 POL-V

0

00

86

88

90

92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.05 .04 .03 .02 .01 .00

ECO-R .2 .1 .0 −.1 −.2 −.3

FIN-V

FIN-R

.2 .1 .0 −.1 −.2 −.3

.08 .06 .04 .02

86

88

90

92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .20

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .08

.15

.04

.10 .04

.05

.03

.00 −.05

.02 .01 .00

86

88

90

92 POL-R

94

96

98

POL-V

00

.006 .005 .004 .003 .002 .001 .000

.00 −.04 −.08

86

88

90

92 COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 231

Figure 4.19. Risk ratings, risk returns and volatilities for Taiwan RISK RATINGS ANDASSOCIATEDVOLATILITIES ECO-V

100 80 60 40 20 0

84

86

ECO-R

88

90

92

ECO-R

94

96

98

92 88 84 80 76 72

00

FIN-V

250 200 150 100 50 0

84

FIN-R

86

88

ECO-V

90

92

94

FIN-R

POL-V

POL-R

96

98

00

FIN-V

COM-V

84

COM-R

80 76 50 40 30 20 10 0

72

40

68

30

100 95 90 85 80 75

88 86 84 82 80 78 76

20 10 84

86

88

90

92

POL-R

94

96

98

0

00

84

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATEDVOLATILITIES ECO-V

.025 .020 .015 .010 .005 .000

84

ECO-R

.12 .08 .04 .00 −.04 −.08 −.12 −.16

86

88

90

92

ECO-R

94

96

98

00

FIN-V

.010 .008 .006 .004 .002 .000

FIN-R

−.05 −.10

84

86

88

ECO-V

90

92

FIN-R

POL-V

POL-R

.08

94

96

98

00

FIN-V

COM-V

COM-R

.04 .006 .005 .004 .003 .002 .001 .000

.00 −.04

84

86

88

90

92

POL-R

94

96

POL-V

98

00

.0024 .0020 .0016 .0012 .0008 .0004 .0000

.10 .05 .00

.06 .04 .02

.00 −.02 −.04

84

86

88

90

92

COM-R

94

96

98

COM-V

00

232

S. Hoti and M. McAleer

Figure 4.20. Risk ratings, risk returns and volatilities for Thailand RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90

ECO-V

FIN-R 90 80 70 60 50 40

FIN-V

80 70 60 500 400 300 200 100 0

50

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R 80

84

86

88

90 92 FIN-R

94 96 98 FIN-V

00

COM-V

COM-R 85 80 75 70 65 60 55

70

250 200

60

150 50

100

150 100 50

50 0

1000 800 600 400 200 0

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .15 .10 .05 .00 −.05 −.10 −.15

ECO-V

.020 .016 .012 .008 .004 .000

FIN-R

FIN-V

.2 .1 .0 −.1 −.2 −.3 −.4

.16 .12 .08 .04

84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-R .10

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .10 .05 .00 −.05 −.10 −.15

COM-V

.05 .00 .010 .008 .006 .004 .002 .000

−.05 −.10

84

86

88

90 92 POL-R

94

96 98 POL-V

00

.020 .016 .012 .008 .004 .000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 233

Figure 4.21. Risk ratings, risk returns and volatilities for Vietnam RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

800 600 400

80 70 60 50 40 30 20

FIN-R

86

88

90

92

94

ECO-R

96

98

70 60

600

50 40

400

0

00

30

86

88

90

92

94

FIN-R

ECO-V

POL-V

POL-R

96

98

00

FIN-V

COM-V

80

COM-R

250 200

50

150

40

100

60

300

50 40

200

30

100

50 0

80 70

70 60

80

800

200

200 0

FIN-V

86

88

90

92

94

POL-R

96

98

0

00

86

88

90

92

94

COM-R

POL-V

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.08 .06

.3 .2 .1 .0 −.1 −.2

.08 .06

.04

.04

.02

.02

.00

86

88

90

92

94

ECO-R

96

98

POL-R

.020 .015 .010 .005 88

90

92

POL-R

94

88

90

96

98

POL-V

92

94

FIN-R

.025

86

86

ECO-V

POL-V

.000

.00

00

FIN-R

FIN-V

00

.16 .12 .08 .04 .00 −.04 −.08

96

98

.2 .1 .0 −.1 −.2 −.3

00

FIN-V

COM-V

COM-R

.12 .08 .04

.012 .010 .008 .006 .004 .002 .000

.00 −.04 −.08

86

88

90

92

94

COM-R

96

98

COM-V

00

234

S. Hoti and M. McAleer

Figure 4.22. Risk ratings, risk returns and volatilities for Albania RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

1000 800 600 400 200 0

ECO-R

90

92

94

ECO-R

96

98

240 200 160 120 80 40 0

70 60

00

500 400 300 200 100 0

50 40

86

88

90

ECO-V

POL-V

92

94

FIN-R POL-R

80

40 0

88

FIN-R

60 20

86

FIN-V

80

75 70 65 60 55 50 45

96

98

00

FIN-V

COM-V

COM-R 70 60 50

400

40

300 200 100

86

88

90

92

94

POL-R

96

98

0

00

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .8

FIN-V

FIN-R

.3 .2 .1 .0 −.1 −.2 −.3

.4 .0 .5 .4 .3 .2 .1 .0

−.4 −.8

.08 .06 .04 .02

86

88

90

92

94

ECO-R

96

98

.00

00

86

88

90

ECO-V

92

94

FIN-R

POL-V

POL-R

.2

96

98

00

FIN-V

COM-V

COM-R .10 .05 .00 −.05 −.10 −.15 −.20

.1 .0 −.1

.020

−.2

.015

.02

.010

.01

.005 .000

.03

86

88

90

92 POL-R

94

96

98

POL-V

00

.00

86

88

90

92 COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 235

Figure 4.23. Risk ratings, risk returns and volatilities for Bulgaria RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

800 600

80 70 60 50 40 30

100 80 60 40

1200

20

400

200 86

88

90

92

94

ECO-R

96

98

0

00

POL-R

60 55

60 40 20 90

92

90

94

POL-R

96

98

92

94

00

96

98

200 160 120 80 40 0

86

00

FIN-V

COM-V

80 75 70 65

88

88

FIN-R

100 80

86

86

ECO-V

POL-V

0

FIN-R

800

400 0

FIN-V

COM-R

88

POL-V

90

92

94

COM-R

96

98

80 75 70 65 60 55 50

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

FIN-V

.6

FIN-R

.6 .4 .2 .0 −.2 −.4 −.6

.4 .2 .0

.25 .20 .15

−.2

.4

−.4

.3

.10

.2

.05

.1

.00

86

88

90

92

94

ECO-R

96

98

.0

00

86

88

90

ECO-V

POL-V

92

94

FIN-R POL-R

96

98

00

FIN-V

COM-V

.12

COM-R .10

.08 .010 .008

−.05

−.08

.004 .002 .000

.00

.00 −.04

.006

86

88

90

92

POL-R

94

96 POL-V

98

00

.05

.04 .010 .008 .006 .004 .002 .000

−.10

86

88

90

92

COM-R

94

96

98

COM-V

00

236

S. Hoti and M. McAleer

Figure 4.24. Risk ratings, risk returns and volatilities for Czech Republic RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 85 80 75 70 65 60

ECO-V

160 120 80 40 0

93

94

95

96

97

ECO-R

98

99

00

93

95

96

97

POL-R

93

94

95

98

99

96

97

FIN-R POL-R 88 84 80 76 72 68

94

200 160 120 80 40 0

ECO-V

POL-V

100 80 60 40 20 0

01

FIN-R

FIN-V

00

01

98

99

00

95 90 85 80 75 70 65

01

FIN-V

COM-V

COM-R 88 84

60 50 40 30 20 10 0

80 76 72

93

94

95

POL-V

96

97

COM-R

98

99

00

01

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

.10 .05 .00 −.05 −.10 −.15

.020 .015 .010 .005 .000

93

94

95

96

97

ECO-R

98

99

00

01

FIN-V

FIN-R

.0 −.1 .10 .08 .06 .04 .02 .00

−.2 −.3

93

94

95

ECO-V

96

97

FIN-R POL-R

POL-V

.08

98

99

00

01

FIN-V COM-R .04

COM-V

.00

.04 .004

.00

.003

−.04

.002

−.08

.001 .000

93

94

95

96

97

POL-R

98

99

POL-V

00

01

.1

−.04 .012 .010 .008 .006 .004 .002 .000

−.08 −.12

93

94

95

96

97

COM-R

98

99

00

COM-V

01

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 237

Figure 4.25. Risk ratings, risk returns and volatilities for Hungary RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 80 70 60 50 40 30

ECO-V

500 400 300 200 100 0

FIN-V

FIN-R 90 80 70

400

60

300

50

200 100 86

88

90

92

94

ECO-R

96

98

0

00

86

88

90

ECO-V

92

94

FIN-R

POL-V

POL-R 90

96

98

00

FIN-V

COM-V

COM-R 85 80 75 70 65 60 55

85 80

120

75 70

80

65

40 0

120 80 40

86

88

90

92

94

POL-R

96

98

0

00

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.2

FIN-V

FIN-R

.0

.0

.04 .03 .02

−.1

.04

−.2

.03

−.1 −.2

.02

.01

.01

.00

.00

86

88

90

92

94

ECO-R

96

98

00

POL-R

.08 .06 .04 .02 .00 −.02 −.04

.003 .002 .001 88

90

92

POL-R

88

90

94

96

98

POL-V

92

94

FIN-R

.004

86

86

ECO-V

POL-V

.000

.2 .1

.1

00

96

98

00

FIN-V

COM-V

COM-R .08 .04 .00

.005 .004 .003 .002 .001 .000

−.04 −.08

86

88

90

92

COM-R

94

96

98

COM-V

00

238

S. Hoti and M. McAleer

Figure 4.26. Risk ratings, risk returns and volatilities for Poland RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80 70 60 50 40 30

ECO-V

800 600 400 200 0

86

88

90

92

94

ECO-R

96

98

00

POL-R 90 80 70 60 50 40

300 200 100 88

90

92

86

88

90

94

POL-R

96

98

92

94

FIN-R

400

86

1000 800 600 400 200 0

ECO-V

POL-V

0

FIN-R 90 80 70 60 50 40 30

FIN-V

00

96

98

00

FIN-V COM-R 90 80 70 60 50 40

COM-V

500 400 300 200 100 0

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.2

FIN-R

FIN-V

.15 .10 .05 .00 −.05 −.10 −.15

.1 .0

.020

−.1

.016

−.2

.012

.03 .02 .01 .00

.008 .004

86

88

90

92

94

ECO-R

96

98

.000

00

86

88

90

ECO-V

92

94

FIN-R POL-R

POL-V

.08

96

98

00

FIN-V COM-R .08

COM-V

.04

.04 .004

.00

.003

−.04

.002

−.08

.001 .000

86

88

90

92 POL-R

94

96

98

POL-V

00

.006 .005 .004 .003 .002 .001 .000

.00 −.04 −.08

86

88

90

92

COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 239

Figure 4.27. Risk ratings, risk returns and volatilities for Romania RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

600 500 400 300 200 100 0

86

ECO-R 70 60 50 40 30 20

88

90

92

94

ECO-R

96

98

00

FIN-V

600 500 400 300 200 100 0

FIN-R 80 70 60 50 40 30 20

86

88

90

ECO-V

92

94

FIN-R

POL-V

POL-R 80

96

98

00

FIN-V

COM-V

COM-R 70 65 60 55 50 45

70 60 50

300

40

200 100 0

150 100 50

86

88

90

92

94

POL-R

96

98

0

00

86

88

POL-V

90

92

94

96

98

00

COM-V

COM-R

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .4 .2 .0 −.2 −.4

.15 .10 .05 .00

86

88

90

92

94

ECO-R

96

98

00

FIN-V

FIN-R

.4 .0 .5 .4 .3 .2 .1 .0

−.4 −.8

86

88

90

92

94

FIN-R

ECO-V

POL-V

POL-R

.12

96

98

00

FIN-V

COM-V

COM-R .10 .05 .00 −.05 −.10 −.15

.08 .04

.010

.00

.008

−.04

.006

−.08

.004 .002 .000

86

88

90

92

POL-R

94

96

98

POL-V

00

.8

.020 .016 .012 .008 .004 .000

86

88

90

92

COM-R

94

96

98

COM-V

00

240

S. Hoti and M. McAleer

Figure 4.28. Risk ratings, risk returns and volatilities for Russia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90 80 70 60 50 40 30

1200 800

FIN-R

400 300 100 0

92 93 94 95 96 97 98 99 00 01 ECO-R

92 93 94 95 96 97 98 99 00 01

ECO-V

POL-V

FIN-R POL-R 70

FIN-V

COM-V

COM-R 72 68 64 60 56 52 48 44

60 50 200

40

150 100 50 0

90 80 70 60 50 40

200

400 0

FIN-V

92 93 94 95 96 97 98 99 00 01 POL-R

240 200 160 120 80 40 0

92 93 94 95 96 97 98 99 00 01

POL-V

COM-R

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

.4 .2 .0 −.2 −.4 −.6

.4 .3 .2 .1 .0

.04 .03 .02 .01 .00

FIN-R POL-R

.2 .1 .0 −.1 −.2

92 93 94 95 96 97 98 99 00 01 POL-R

POL-V

.2 .1 .0 −.1 −.2 −.3 −.4

92 93 94 95 96 97 98 99 00 01

ECO-V

POL-V

FIN-R

.16 .12 .08 .04 .00

92 93 94 95 96 97 98 99 00 01 ECO-R

FIN-V

FIN-V

COM-V

COM-R .1 .0 −.1

.05 .04 .03 .02 .01 .00

−.2 −.3

92 93 94 95 96 97 98 99 00 01 COM-R

COM-V

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 241

Figure 4.29. Risk ratings, risk returns and volatilities for Slovak Republic RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90

ECO-V 250

80

200

70

150

60

100

50

50 0

93

94

95

96

97

98

ECO-R

99

00

01

60 40 20 94

95

96

97

POL-R

93

94

95

98

99

00

96

97

98

FIN-R POL-R 90 85 80 75 70 65

80

93

100 80 60 40 20 0

ECO-V

POL-V

0

FIN-R 84 80 76 72 68 64

FIN-V

01

99

00

COM-R 80 78 76 74 72 70 68

COM-V

30 25 20 15 10 5 0

93

01

FIN-V

94

95

POL-V

96

97

COM-R

98

99

00

01

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .3 .2 .1 .0 −.1 −.2 −.3

ECO-V

.08 .06 .04 .02 .00

93

94

95

96

97

ECO-R

98

99

00

01

.010 .008 .006 .004 .002 .000

POL-R

.06 .04 .02 .00 −.02 −.04

.0015

.002 97

POL-R

98

99

POL-V

97

00

01

98

99

00

.000

93

01

FIN-V COM-R .12 .08 .04 .00 −.04 −.08

.006 .004

96

96

.008

.0005 95

95

COM-V

.0010

94

94

FIN-R

.0020

93

93

.08 .04 .00 −.04 −.08 −.12

ECO-V

POL-V

.0000

FIN-R

FIN-V

94

95

96

97

COM-R

98

99

00

COM-V

01

242

S. Hoti and M. McAleer

Figure 4.30. Risk ratings, risk returns and volatilities for Yugoslavia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

500 400 300 200 100 0

84

ECO-R 70 60 50 40 30 20

86

88

90

92

94

ECO-R

96

98

00

FIN-R 60 50 40

300 250 200 150 100 50 0

30 20

84

86

88

90

92

94

FIN-R

ECO-V

POL-V

POL-R 70 60 50 40 30 20 10

800 600 400

96

98

00

FIN-V

COM-V

COM-R 60 55 50 45 40 35 30

200 150 100

200 0

FIN-V

50 84

86

88

90

92

94

POL-R

96

98

0

00

84

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .6

FIN-V

FIN-R

.6 .4 .2 .0 −.2 −.4 −.6

.4 .2

.4

.0

.3

−.2

.2

−.4

.1 .0

.2 .1

84

86

88

90

92

94

ECO-R

96

98

.6 .4 .2 .0 −.2 −.4

.2 .1 88

90

92

POL-R

86

88

94

96 POL-V

98

90

92

94

FIN-R POL-R

86

84

ECO-V

.3

84

.0

00

POL-V

.0

.3

00

96

98

00

FIN-V

COM-V

.05 .04 .03 .02 .01 .00

84

COM-R .3 .2 .1 .0 −.1 −.2 −.3

86

88

90

92

COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 243

Figure 4.31. Risk ratings, risk returns and volatilities for Algeria RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90

800

80

600

70 60

400

50

200 0

40 84

86

88

90

92

94

ECO-R

96

98

FIN-V

80 500

70

400

60

300

50

200

40

100 0

00

FIN-R 90

84

86

88

ECO-V

90

92

94

FIN-R

POL-V

POL-R 70

96

98

00

FIN-V

COM-V

COM-R 65

60 50

200

40

150

30

100

55

60 40

50

20

50 0

60

80

84

86

88

90

92

94

POL-R

96

98

0

00

84

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .2

ECO-V

FIN-R

FIN-V

.15 .10 .05 .00 −.05 −.10 −.15

.1 .0 −.1

.03

−.2

.02 .01 .00

84

86

88

90

92

94

ECO-R

96

98

00

POL-R

.15 .10 .05 .00 −.05 −.10

.008 .004 86

88

90

92

POL-R

86

88

94

96 POL-V

90

92

94

FIN-R

.012

84

84

ECO-V

POL-V

.000

.020 .016 .012 .008 .004 .000

98

00

96

98

00

FIN-V

COM-V

COM-R .08 .04 .00

.005 .004 .003 .002 .001 .000

−.04 −.08

84

86

88

90

92

COM-R

94

96

98

COM-V

00

244

S. Hoti and M. McAleer

Figure 4.32. Risk ratings, risk returns and volatilities for Bahrain RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 85 80 75 70 65 60

ECO-V

400 300 200 100 0

86

88

90

92

94

ECO-R

96

98

00

80 70 300 250 200 150 100 50

60 50

86

88

90

ECO-V

92

94

FIN-R

POL-V

POL-R 80

96

98

00

FIN-V

COM-V

COM-R 80 75

70

300

60

200

50 100 0

FIN-R 90

FIN-V

40 86

88

90

92

94

POL-R

96

98

65

120

60

80

55

40 0

00

70

160

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.2

FIN-R

FIN-V

.4 .3 .2 .1 .0 −.1

.1 .0 −.1 .04

−.2

.03 .02 .01 .00

86

88

90

92

94

ECO-R

96

98

00

.10 .08 .06 .04 .02 .00

86

88

90

ECO-V

92

94

FIN-R

POL-V

POL-R .3

96

98

00

FIN-V

COM-V

COM-R .12 .08 .04 .00 −.04 −.08

.2 .1 .05 .04 .03 .02 .01 .00

.0 −.1

86

88

90

92

POL-R

94

96

98

POL-V

00

.010 .008 .006 .004 .002 .000

86

88

90

92

COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 245

Figure 4.33. Risk ratings, risk returns and volatilities for Egypt RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90 80 70 60 50 40 30

800 600

FIN-V

800 600

400

400

200

200

0

84

86

88

90

92

94

ECO-R

96

98

0

00

FIN-R 90 80 70 60 50 40 30

84

86

88

ECO-V

90

92

94

FIN-R

POL-V

POL-R 70

96

98

00

FIN-V

COM-V

COM-R 80 70 60 50 40 30

60 50 280 240 200 160 120 80 40 0

40 30

84

86

88

90

92

94

POL-R

96

98

00

500 400 300 200 100 0

84

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.25 .20 .15 .10 .05 .00

ECO-R

.4 .2 .0 −.2 −.4 −.6

FIN-R

.3 .2 .1 .0 −.1 −.2

.04 .03 .02 .01

84

86

88

90

92

94

ECO-R

96

98

.10 .05 .00 −.05 −.10 −.15

.012 .008 .004 88

90

92

POL-R

86

88

94

96 POL-V

90

92

94

FIN-R POL-R

86

84

ECO-V

.016

84

.00

00

POL-V

.000

FIN-V

98

00

96

98

00

FIN-V

COM-V

.025 .020 .015 .010 .005 .000

84

COM-R .10 .05 .00 −.05 −.10 −.15

86

88

90

92

COM-R

94

96

98

COM-V

00

246

S. Hoti and M. McAleer

Figure 4.34. Risk ratings, risk returns and volatilities for Iran RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 80 70 60 50 40 30

ECO-V

600 500 400 300 200 100 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 80 70 60 50 40 30 20

POL-V

800 600 400 200 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

FIN-R 100

FIN-V

80 60 1200 1000 800 600 400 200 0

40 20

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-R 80 70 60 50 40 30

COM-V

600 500 400 300 200 100 0

84

00

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.3

FIN-R

FIN-V

.2 .1

.08 .06 .04

.00

.04

−.1

.03

−.2

.02

.0 −.1 −.2

.01 84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-R

POL-V

.030 .025 .020 .015 .010 .005 .000

.1

.0

.02

.2

.20 .15 .10 .05 .00 −.05 −.10

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .15

COM-V

.10 .05

.020 .016

.00

.012

−.05

.008

−.10

.004 84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 247

Figure 4.35. Risk ratings, risk returns and volatilities for Iraq RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 60

FIN-V

FIN-R 80

50 40 30 500 400 300 200 100 0

20

40

1200

20

800

0

400 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 45 40 35 30 25 20 15

300

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 50 40 30

250

20

200

10

150

200

100

100 0

60

1600

50 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .8

FIN-V

FIN-R

.0 .6 .5 .4 .3 .2 .1 .0

−.4 −.8

0.5

1.6

0.0

1.2

−0.5

0.8

−1.0

0.4 84

86

88

90 92 94 96 98 ECO-R ECO-V

0.0

00

POL-V

POL-R

.4

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .3 .2 .1 .0 −.1 −.2 −.3

.2 .16

.0

.12

−.2

.08

−.4

.04 .00

1.5 1.0

.4

.08 .06 .04 .02

84

86

88

90 92 94 96 98 POL-R POL-V

00

.00

84

86

88

90 92 94 96 98 COM-R COM-V

00

248

S. Hoti and M. McAleer

Figure 4.36. Risk ratings, risk returns and volatilities for Israel RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90

ECO-V

FIN-R 90 80 70 60 50 40

FIN-V

80 70 300 250 200 150 100 50 0

60 50

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 80 70 60 50 40 30

POL-V

500 400 300 200 100 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

500 400 300 200 100 0

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80

COM-V

70 60 250 200 150 100 50 0

50 40

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .2

ECO-V

FIN-R

FIN-V

.20 .15 .10 .05 .00 –.05 –.10

.1 .0 .025 .020 .015 .010 .005 .000

–.1

.03

–.2

.02 .01

84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-R .2

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .12

COM-V

.08

.1 .04 .03 .02

.012

–.1

.008

.00 –.04 –.08

.004

.01 .00

.04

.0

84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 249

Figure 4.37. Risk ratings, risk returns and volatilities for Jordan RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 85 80 75 70 65 60 55

150 100

800 600 200

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 80 70 60 50 40 30

800 600 400 200 0

FIN-R 90 80 70 60 50 40 30

400

50 0

FIN-V

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 80 70 60

500 400 300 200 100 0

50 40

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.020 .016 .012 .008 .004 .000

ECO-R

.15 .10 .05 .00 −.05 −.10 −.15

FIN-V

FIN-R

.4 .3 .2 .1 .0 −.1 −.2

.15 .10 .05

84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R

.3

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .3

.2

.08 .06 .04

.0

.08

−.1

.06

−.2

.04

.1 .0 −.1

.02

.02 .00

.2

.1

.10

84

86

88

90 92 94 96 98 POL-R POL-V

00

.00

84

86

88

90 92 94 96 98 COM-R COM-V

00

250

S. Hoti and M. McAleer

Figure 4.38. Risk ratings, risk returns and volatilities for Kuwait RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 100 90 80 70 60 50 40

1500 1000 500 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 80

POL-V

1000 500 90 92 94 96 98 POL-R POL-V

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 100

COM-V

60

1500

88

86

80

0

86

84

40 20

84

5000 4000 3000 2000 1000 0

60 2000

0

FIN-R 100 80 60 40 20 0

FIN-V

00

2500 2000 1500 1000 500 0

40 20

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .4 .2 .0 −.2 −.4 −.6

ECO-V

.4 .3 .2

84

86

88

90 92 94 96 98 ECO-R ECO-V

1 0 −1 3

−2

2

0

00

POL-R

POL-V

.8

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .8

COM-V

.4

.4

.0

.0

.4

−.4

.3

−.8

.2 .1 .0

2

1

.1 .0

FIN-R

FIN-V

.4

−.4

.3

−.8

.2 .1

84

86

88

90 92 94 96 98 POL-R POL-V

00

.0

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 251

Figure 4.39. Risk ratings, risk returns and volatilities for Lebanon RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

500 400 300 200 100 0

ECO-R 80 70 60 50 40 30

FIN-V

FIN-R 80 60 40

1000 800

20 0

600 400 200

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 80

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 70 60 50 40 30 20

60 40 1200 1000 800 600 400 200 0

20 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

600 500 400 300 200 100 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .4

FIN-V

FIN-R

.2 .0

.15

−.2

.10

−.4 .05 .00

84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

POL-R .4

.0 −.2

.25 .20 .15

−.4

.10 .05 .00

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .2

.2 .0 .12 .10 .08 .06 .04 .02 .00

−.2 −.4

.1 .04

.0

.03

−.1

.02

−.2

.01 84

86

88

90 92 94 96 98 POL-R POL-V

00

.6 .4 .2

.00

84

86

88

90 92 94 96 98 COM-R COM-V

00

252

S. Hoti and M. McAleer

Figure 4.40. Risk ratings, risk returns and volatilities for Libya RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 80 70 60 50 40 30

ECO-V

800 600 400 200 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

500 400 300 200 100 0

POL-R 80 70 60 50 40 30

FIN-V

1000 800 600 400 200 0

84

FIN-R 90 80 70 60 50 40 30

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 80 70 60 50 40 30

400 300 200 100

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .3 .2 .1 .0 −.1 −.2 −.3

ECO-V

.10 .08 .06 .04 .02 .00

FIN-R

FIN-V

.3 .2 .1 .0 −.1 −.2

.08 .06 .04

−.3

.02 84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .3

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .16 .12 .08 .04 .00 −.04 −.08

.2 .1 .0 .06 .05 .04 .03 .02 .01 .00

−.1

84

86

88

90 92 94 96 98 POL-R POL-V

00

.024 .020 .016 .012 .008 .004 .000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 253

Figure 4.41. Risk ratings, risk returns and volatilities for Morocco RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80 75 70 65 60 55 50

200 150 100 50 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

500 400 300 200 100 0

POL-R 80 70 60 50 40 30

FIN-V

FIN-R 80 70 60

500 400 300 200 100 0

50 40

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 80 70 60

400

50

300

40

200 100 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.05 .04 .03 .02 .01 .00

ECO-R .2 .1 .0 −.1 −.2 −.3

FIN-V

FIN-R

.2 .1 .08

.0

.06

−.1 −.2

.04 .02

84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .3

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .3 .2

.2 .1

.08

.0

.06

−.1

.04

.1

.05 .04

.0

.03

−.1

.02

.02 .00

.3

.01 84

86

88

90 92 94 96 98 POL-R POL-V

00

.00

84

86

88

90 92 94 96 98 COM-R COM-V

00

254

S. Hoti and M. McAleer

Figure 4.42. Risk ratings, risk returns and volatilities for Oman RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 100 90 80 70 60 50

400 300 200 100 0

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R 80 75 70 65 60 55 50

150 100 50 0

86

88

90 92 94 96 98 POL-V POL-R

00

FIN-V

FIN-R 90 80 70

300 250 200 150 100 50 0

60 50

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

200 160 120 80 40 0

86

COM-R 85 80 75 70 65 60 55

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .4 .3 .2 .1 .0 −.1 −.2

ECO-V

.10 .08 .06 .04

.03 .02

.02

.01 .00

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .08

.20 .16 .12 .08 .04 .00 −.04 −.08

.04

.00

86

FIN-R

FIN-V

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .12 .08

.04 .006 .005 .004 .003 .002 .001 .000

.00

.008

−.04

.006

−.08

.04 .00 −.04

.004

−.08

.002 86

88

90 92 94 96 98 POL-R POL-V

00

.000

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 255

Figure 4.43. Risk ratings, risk returns and volatilities for Qatar RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 100 90 80 70 60 50 40

800 600 400

FIN-R 90 80

400

70

300

60

200

50

100

200 0

FIN-V

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 90

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 80 76 72 68 64 60 56 52

80 70 400

60

250

300

50

200

40

150

200

100

100 0

50 86

88

90 92 94 96 98 POL-R POL-V

0

00

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .3

ECO-V

FIN-R

FIN-V

.3 .2 .1 .0 −.1 −.2 −.3

.2 .1 .08

.0

.06

−.1 −.2

.04 .02 .00

.08 .06 .04 .02

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .15

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .15 .10

.10 .05

.020

.00

.016

−.05

.012

−.10

.008

.00 −.05

.010

−.10

.005

.004 .000

.05 .015

86

88

90 92 94 96 98 POL-R POL-V

00

.000

86

88

90 92 94 96 98 COM-R COM-V

00

256

S. Hoti and M. McAleer

Figure 4.44. Risk ratings, risk returns and volatilities for Saudi Arabia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90

FIN-V

FIN-R 100 90 80 70 60 50 40

80 70 60

400

50

300

800 600

200

400

100

200

0

84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

250 200 150 100 50 0

0

00

POL-R 80

88

90 92 94 96 98 POL-R POL-V

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 90 80

60

70

40

86

86

70 50

84

84

00

60 240 200 160 120 80 40 0

50

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.05 .04 .03 .02 .01 .00

84

ECO-R

.3 .2 .1 .0 −.1 −.2

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

00

FIN-V

.06 .05 .04 .03 .02 .01 .00

84

FIN-R

.2 .1 .0 −.1 −.2 −.3

86

88

90

92

FIN-R

POL-R

.2

94

96

98

00

FIN-V

COM-V

COM-R .15 .10

.1 .0

.04

−.1

.03 .02

.05

.020

.00

.016

−.05

.012

−.10

.008

.01

.004

.00

.000

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 257

Figure 4.45. Risk ratings, risk returns and volatilities for Syria RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 80 70 60 50 40 30

ECO-V

500 400 300 200 100 0

800 600 400

80 70 60 50 40 30 20

200 84

86

88

90 92 94 96 98 ECO-R ECO-V

84

0

00

POL-R 80 70 60 50 40 30 20

POL-V

1000 800 600 400 200 0

FIN-R

FIN-V

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-R 80 70 60 50 40 30

COM-V

600 500 400 300 200 100 0

84

00

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.3

FIN-R

FIN-V

.4

.2

.2

.1 .05

.0

.04

−.1

.03

−.2

.02 .01 .00

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R

POL-V

.15 .10 .05 .00 −.05 −.10

.016 .012 .008 .004 .000

84

86

88

90 92 94 96 98 POL-R POL-V

00

.12 .10 .08 .06 .04 .02 .00

.0 −.2 −.4

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-R

COM-V

.010 .008 .006 .004 .002 .000

84

00

.15 .10 .05 .00 −.05 −.10

86

88

90 92 94 96 98 COM-R COM-V

00

258

S. Hoti and M. McAleer

Figure 4.46. Risk ratings, risk returns and volatilities for Tunisia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 80 70

70 60 300 250 200 150 100 50 0

50

60 400

50

300

40

200 100 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 80

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80

COM-V

70

70

60 400 300

50

400

40

300

200

200

100

100

0

84

86

88

90 92 94 96 98 POL-R POL-R

0

00

60 50 40

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.4 .3 .2 .1 .0 −.1 −.2

.1 .0 −.1

.04

−.2

.03

.04

.01 84

86

88

90 92 94 96 98 ECO-R ECO-V

84

.00

00

POL-R .16 .12 .08 .04 .00 −.04 −.08

POL-V

.025 .020 .015 .010 .005 .000

.12 .08

.02 .00

.16

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .20 .15 .10 .05 .00 −.05 −.10

COM-V

.025 .020 .015 .010 .005 .000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 259

Figure 4.47. Risk ratings, risk returns and volatilities for the UAE RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 95 90 85 80 75 70

120

800 400

40

200 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

84

86

88

90

92

94

FIN-R POL-R 90 80 70 60 50 40

POL-V

500 400 300 200 100 0

FIN-R 100 90 80 70 60 50 40

600

80

0

FIN-V

96

98

00

FIN-V COM-R 90

COM-V

80 70

400

60

300

50

200 100

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .15 .10 .05 .00 −.05 −.10

.020 .015 .010

FIN-R

84

86

88

90 92 94 96 98 ECO-R ECO-V

.1 .04

.0

.03

−.1

.02

−.2

.00

00

84

86

88

90

92

FIN-R POL-R .3

POL-V

94

96

98

00

FIN-V COM-R .20 .15 .10 .05 .00 −.05 −.10

COM-V

.2 .1 .05 .04 .03 .02 .01 .00

.2

.01

.005 .000

FIN-V

.0 −.1

84

86

88

90 92 94 96 98 POL-R POL-V

00

.030 .025 .020 .015 .010 .005 .000

84

86

88

90 92 94 96 98 COM-R COM-V

00

260

S. Hoti and M. McAleer

Figure 4.48. Risk ratings, risk returns and volatilities for Yemen RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80 70 60 50 40 30 20

ECO-V

1600 1200 800 400 0

1992

1994 1996 ECO-R

1998 2000 ECO-V

POL-V

POL-R 70 65 60 55 50 45

150 100 50 0

1992

1994 1996 POL-R

1998 2000 POL-V

FIN-R 80

FIN-V

70 60 500 400 300 200 100 0

50 40

1992

1994 1996 1998 2000 FIN-R VYEM_FIN

COM-V

COM-R 70 60

500 400 300 200 100 0

50 40

1992

1994 1996 COM-R

1998 2000 COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 0.4

FIN-V

FIN-R

.0

−0.4 1.0 0.8 0.6 0.4 0.2 0.0

−0.8

.16

−1.2

.12

−.2 −.4

.08 .04 1992

1994 1996 ECO-R

.00

1998 2000 ECO-V

POL-V

POL-R .3

1992

1994 1996 FIN-R

1998 FIN-V

2000

COM-V

COM-R .3

.2 .1

.05

.0

.04 .03 .02

.2 .1

.08

.0

−.1

.06

−.1

−.2

.04

−.2

.02

.01 .00

.4 .2

0.0

1992

1994 1996 POL-R

1998 2000 POL-V

.00

1992

1994 1996 COM-R

1998 2000 COM-V

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 261

Figure 4.49. Risk ratings, risk returns and volatilities for the Bahamas RISK RATINGS AND ASSOCIATED VOLATILITES ECO-V

100 80 60 40 20 0

86

ECO-R 85 80 75 70 65 60

88

90 92 94 96 98 ECO-R ECO-V

POL-R 85

160

80

120

75

80 40 0

86

88

90

92 94 96 98 POL-R POL-V

FIN-R 90 85 80 75 70 65 60

200 150 100 50 0

00

POL-V

FIN-V

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 84

100

80

80

70

60

76

65

40

72

60

20 0

00

68 86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITES ECO-V

.006 .005 .004 .003 .002 .001 .000

ECO-R .08

FIN-R

.1

.04

.0

.00

−.1

−.04 −.08

.08

−.2

.06

−.3

.04 .02 86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

.0025 .0020 .0015 .0010 .0005 .0000

FIN-V

POL-R

.06 .04 .02 .00 −.02 −.04

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .06 .04 .02 .00 −.02 −.04 −.06

.004 .003 .002 .001

86

88

90

92 94 96 98 POL-R POL-V

00

.000

86

88

90 92 94 96 98 COM-R COM-V

00

262

S. Hoti and M. McAleer

Figure 4.50. Risk ratings, risk returns and volatilities for Canada RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 88

100

84

80

80

60

76

40

72

20 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R 92

FIN-V

240 200 160 120 80 40 0

84

FIN-R 100 95 90 85 80 75 70

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 88 86

88 60 50 40 30 20 10 0

84 80 76

84

15

82

10

80

5 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .12 .08

.008

.04 .00

.006

−.04 −.08

.004 .002 .000

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .04

FIN-V

.024 .020 .016 .012 .008 .004 .000

84

FIN-R

.05 .00 −.05 −.10 −.15 −.20

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .03 .02 .01 .00 −.01 −.02

.02 .00 .0016

−.02

.0012

−.04

.0008 .0004 .0000

84

86

88

90 92 94 96 98 POL-R POL-V

00

.0005 .0004 .0003 .0002 .0001 .0000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 263

Figure 4.51. Risk ratings, risk returns and volatilities for Costa Rica RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

600 500 400 300 200 100 0

84

ECO-R 80 70 60 50 40 30

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R 85

FIN-V

600 500 400 300 200 100 0

84

FIN-R 90 80 70 60 50 40 30

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 80 75 70 65 60 55 50

80 75

160

70

120

65

80

60

40 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

250 200 150 100 50 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .3

ECO-V

FIN-R

FIN-V

.2 .1 .05

.0

.04

−.1

.03

−.2

.02

.08 .04

.008

.00

.006

−.04

.004

−.08

.002

.01 .00

.12

84

86

88

90 92 94 96 98 ECO-R ECO-V

.000

00

POL-V

POL-R .08

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .06 .04

.04 .005

.02

.00

.0025

.00

.003

−.04

.0020

−.02

.002

−.08

.0015

−.04

.0010

.004

.001

.0005

.000

.0000

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

264

S. Hoti and M. McAleer

Figure 4.52. Risk ratings, risk returns and volatilities for Cuba RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80 70 60 50 40 30 20

ECO-V

800 600 400

60 50 40 300

30

200 100

200 0

FIN-R 70

FIN-V

86

88

90

92 94 ECO-R

96 98 ECO-V

0

00

POL-R 70

POL-V

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-R 70 65 60 55 50 45 40

COM-V

65 100

60

80

55

60

50

40 20 0

160 120 80 40

86

88

90

92 94 POL-R

96 98 POL-V

0

00

86

88

90

92 94 COM-R

96 98 00 COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .3 .2 .1 .0 −.1 −.2 −.3

ECO-V

.05 .04 .03 .02 .01 .00

FIN-R

FIN-V

.4 .2 .0

−.2

.12

−.4

.08 .04

86

88

90

92 94 ECO-R

96 98 ECO-V

.00

00

POL-R

POL-V

.12

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-R .15

COM-V

.10

.08

.05

.04

.020

.008

.00

.016

.00

.006

−.04

.012

−.05

.008

−.10

.010

.004 .002

.004

.000

.000

86

88

90

92 94 POL-R

96 98 POL-V

00

86

88

90

92 94 COM-R

96 98 COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 265

Figure 4.53. Risk ratings, risk returns and volatilities for Dominican Republic RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80

ECO-V

FIN-R 80 70 60 50 40 30 20

FIN-V

70 60 500 400 300 200 100 0

50 40

600 400 200

84

86

88

90 92 ECO-R

94

96 98 ECO-V

0

00

POL-R 75 70 65 60 55 50 45

POL-V

200 150 100 50 0

800

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80

COM-V

70 60 250 200 150 100 50 0

50 40

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.05 .04 .03 .02 .01 .00

.2 .1 .0 −.1 −.2 −.3

FIN-R

FIN-V

.3 .2 .1 .0 −.1 −.2

.08 .06 .04 .02

84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-R

POL-V

.08

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .08

COM-V

.04

.04 .006 .005 .004 .003 .002 .001 .000

.00 −.04 −.08

84

86

88

90 92 94 96 98 POL-R POL-V

00

.00 .006 .005 .004 .003 .002 .001 .000

−.04 −.08

84

86

88

90 92 94 96 98 COM-R COM-V

00

266

S. Hoti and M. McAleer

Figure 4.54. Risk ratings, risk returns and volatilities for El Salvador RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 90 80 70 60 50 40 30

70 60 600 500 400 300 200 100 0

50 40

800 600 400 200

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 80 70 60 50 40 30 20

POL-V

800 600 400 200 0

1000

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80 70 60 50 40 30

COM-V

500 400 300 200 100 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2 .1

.024 .020 .016 .012 .008 .004 .000

.0 −.1 −.2

FIN-R

84

86

88

90 92 94 96 98 ECO-R ECO-V

.0

.03

−.1

.02

84

.00

00

POL-R .15 .10 .05 .00 −.05 −.10 −.15

86

88

90 92 94 96 98 POL-R POL-V

00

.2 .1

.04

−.2

.01

POL-V

.020 .016 .012 .008 .004 .000

FIN-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .12

COM-V

.08 .006 .005 .004 .003 .002 .001 .000

.04 .00 −.04 −.08

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 267

Figure 4.55. Risk ratings, risk returns and volatilities for Guatemala RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 90 80 70 60 50 40 30

70 60 400

50

300

40

200

84

86

88

90 92 94 96 98 ECO-R ECO-V

400

0

00

POL-R 80 70 60 50 40 30 20

POL-V

500 400 300 200 100 0

600

200

100 0

800

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80 70 60 50 40 30

COM-V

400 300 200 100

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.06 .05 .04 .03 .02 .01 .00

ECO-R .2 .1 .0 −.1 −.2 −.3

FIN-V

FIN-R

.3 .2 .1

.16

.0

.12

−.1

.08 .04 84

86

88

90 92 ECO-R

94

96 98 ECO-V

.00

00

POL-R .08

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .10

COM-V

.04

.05

.00 .005 .004 .003 .002 .001 .000

.4

.00

−.04

.010

−.08

.008

−.05

.006

−.10

.004 .002 84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

268

S. Hoti and M. McAleer

Figure 4.56. Risk ratings, risk returns and volatilities for Haiti RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 70 65 60 55 50 45

ECO-V

100 80 60 40 20 0

FIN-R 80

FIN-V 1600

60

1200

40

800

20

400

84

86

88

90 92 ECO-R

94

96 98 ECO-V

0

00

POL-R 60

POL-V

0 84

86

88

90 92 FIN-R

94

96 98 FIN-V

00

COM-R 60

COM-V

50 40

400

30

300

20

200 100 0

84

86

88

90 92 POL-R

94

96 98 POL-V

00

50 280 240 200 160 120 80 40 0

40 30 20

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.2

FIN-R

FIN-V

.1 .0 .024 .020 .016 .012 .008 .004 .000

.08

−.2

.06 .04 .02

84

86

88

90 92 ECO-R

94

96 98 ECO-V

.00

00

POL-R

POL-V

.3 .2 .1 .0 −.1 −.2 −.3

.08 .06 .04 .02 .00

−.1

.3 .2 .1 .0 −.1 −.2 −.3

84

86

88

90 92 FIN-R

94

96 98 FIN-V

00

COM-R .2

COM-V

.1 .0 .020

−.1

.015

−.2

.010 .005

84

86

88

90 92 POL-R

94

96 98 POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 269

Figure 4.57. Risk ratings, risk returns and volatilities for Honduras RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

200 160 120 80 40 0

ECO-R 70 65 60 55 50 45 40

FIN-V

FIN-R 80 70 60 50

300

40

200

30

100 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 70

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 70

COM-V

60

60

50 300 200

200

40

30

150

30

100

100 0

50

40

50 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .4

FIN-V

FIN-R

.2

.1

.0 .10 .08 .06 .04 .02 .00

−.2 −.4

.0 .04

−.1

.03

−.2

.02 .01 84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-R .15

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .08 .04 .00 −.04 −.08 −.12

COM-V

.10 .05 .00

.015

−.05

.010 .005 .000

.2

84

86

88

90 92 94 96 98 POL-R POL-V

00

.010 .008 .006 .004 .002 .000

84

86

88

90 92 94 96 98 COM-R COM-V

00

270

S. Hoti and M. McAleer

Figure 4.58. Risk ratings, risk returns and volatilities for Jamaica RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-R 90 80 70 60 50 40 30

70

400

60

300 200 100 0

FIN-V

800

50

600

40

400 200

84

86

88

90 92 ECO-R

94

96 98 ECO-V

POL-V

0

00

POL-R 90

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-V

00

COM-R 80

80

70

70 60

200

50

150 100 50 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

60 250 200 150 100 50 0

50 40

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.05 .04 .03 .02 .01 .00

ECO-R

.2 .1 .0 −.1 −.2 −.3

FIN-V

FIN-R

.10 .05 .00 −.05 −.10 −.15

.012 .008 .004

84

86

88

90 92 ECO-R

94

96 98 ECO-V

POL-V

.000

00

POL-R

.12

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-V

00

COM-R .08 .04

.08

.00

.04

.008 .006 .004

.00

.003

−.04

.002

.002

.001

.000

.000

84

86

88

90 92 94 96 98 POL-R POL-V

00

−.04 −.08

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 271

Figure 4.59. Risk ratings, risk returns and volatilities for Mexico RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90

ECO-V

FIN-R 90 80 70 60 50 40 30

FIN-V

80 70 400

60

300

50 40

200 100 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 80

POL-V

1000 800 600 400 200 0

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80 75 70 65 60 55 50

COM-V

75 70

100

65

80

60

60

55

40 20 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

250 200 150 100 50 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .2

ECO-V

FIN-R

FIN-V

.2 .1 .0 −.1 −.2 −.3

.1 .0

.04

−.1

.03

−.2

.02 .01 .00

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R

POL-V

.12

.05 .04 .03 .02 .01 .00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .08 .04 .00 −.04 −.08 −.12

COM-V

.08 .04

.010

.00

.008

−.04

.006

−.08

.004 .002 .000

84

86

88

90 92 94 96 98 POL-R POL-V

00

.010 .008 .006 .004 .002 .000

84

86

88

90 92 94 96 98 COM-R COM-V

00

272

S. Hoti and M. McAleer

Figure 4.60. Risk ratings, risk returns and volatilities for Nicaragua RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 70 60 50 40 30 20

60 40 20 1000 800 600 400 200 0

0

400 300 200 100

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 70

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 70 60 50 40 30 20

60 50 40

400

30

300

400 300

200

200

100

100

0

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R 1.5

ECO-V

FIN-R

FIN-V

.4 .2 .0 −.2 −.4 −.6

1.0 0.5

1.6

0.0

1.2

−0.5

0.8

−1.0

0.4 0.0

.3 .2 .1

84

86

88

90 92 94 96 98 ECO-R ECO-V

84

.0

00

POL-V

.025 .020 .015 .010 .005 .000

.4

POL-R .20 .15 .10 .05 .00 −.05 −.10

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .2 .1 .0

.024 .020 .016 .012 .008 .004 .000

−.1 −.2

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 273

Figure 4.61. Risk ratings, risk returns and volatilities for Panama RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-R 80 70 60 50 40 30

76

50 40

72

30

68

20

64

10 0

FIN-V

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 80

POL-V

500 400 300 200 100 0

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80

COM-V

70 500

60

400

50

300

40

200

30

100 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

70 60 250 200 150 100 50 0

50 40

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .12

FIN-V

FIN-R

.08 .04 .008

.00

.006

−.04 −.08

.004

.2

00

POL-R .20 .15 .10 .05 .00 −.05 −.10

POL-V

.025 .020 .015 .010 .005 .000

84

86

88

90 92 94 96 98 POL-R POL-R

00

−.2

.02 .01

90 92 94 96 98 ECO-R ECO-V

−.1

.03

.00

88

.0

.04

.000

86

.1

.05

.002 84

.3

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .08

COM-V

.04 .006 .005 .004 .003 .002 .001 .000

.00 −.04 −.08

84

86

88

90 92 94 96 98 COM-R COM-V

00

274

S. Hoti and M. McAleer

Figure 4.62. Risk ratings, risk returns and volatilities for Trinidad and Tobago RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90

FIN-V

FIN-R 90 80 70 60 50 40

80 400

70

300

60

200

50

100 0

300 200 100

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 80 75 70 65 60 55 50

POL-V

200 150 100 50 0

400

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80 75 70 65 60 55 50

COM-V

200 160 120 80 40

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.020 .016 .012 .008 .004 .000

84

ECO-R .15 .10 .05 .00 −.05 −.10 −.15

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R .10

POL-V

FIN-V

FIN-R

.1 .0 .025 .020 .015 .010 .005 .000

−.1 −.2

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .08

COM-V

.05

.008 .004 .002 .000

84

86

88

90 92 94 96 98 POL-R POL-V

00

.04

.00

.00

−.05

−.04

−.10

.006

.2

.006 .005 .004 .003 .002 .001 .000

−.08

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 275

Figure 4.63. Risk ratings, risk returns and volatilities for the USA RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

200 160 120 80 40 0

ECO-R 85 80 75 70 65 60

FIN-R 100 90 80 70 60 50

1600 1200 800 400

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 96 92 88 84 80 76 72

160 120 80 40 0

FIN-V

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 92 88

60 50 40 30 20 10 0

84 80 76

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.3 .2 .1 .0 −.1 −.2 −.3

.1 .0

.04 .03 .02

−.1

.08

−.2

.06 .04

.01 .00

.02 84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

POL-R .08 .04 .00 −.04 −.08 −.12

.008 .006 .004 .002 .000

84

.00

00

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

.0030 .0025 .0020 .0015 .0010 .0005 .0000

84

COM-R .06 .04 .02 .00 −.02 −.04 −.06

86

88

90 92 94 96 98 COM-R COM-V

00

276

S. Hoti and M. McAleer

Figure 4.64. Risk ratings, risk returns and volatilities for Argentina RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 100

FIN-V

FIN-R 80

80

60

60 1200 1000 800 600 400 200 0

40

1600

20

1200

40 20 0

800 400 84

86

88

90 92 ECO-R

94 96 98 ECO-V

0

00

POL-V

POL-R 80

84

86

88

90 92 FIN-R

94 96 98 FIN-V

00

COM-V

COM-R 80 70 60 50 40 30

70 60 280 240 200 160 120 80 40 0

50 40

84

86

88

90

92

POL-R

94

96

98

00

600 500 400 300 200 100 0

84

86

88

POL-V

90

92

COM-R

94

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .3 .2 .1 .0 −.1 −.2 −.3

.08 .06 .04 .02 .00

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .08 .04 .00 −.04 −.08 −.12

.008 .006 .004 .002 .000

84

86

88

90 92 94 96 98 POL-R POL-V

00

FIN-V

FIN-R

.2 .0

−.2 .30 .25 .20 .15 .10 .05 .00

−.4 −.6

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

.020 .016 .012 .008 .004 .000

84

COM-R .10 .05 .00 −.05 −.10 −.15

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 277

Figure 4.65. Risk ratings, risk returns and volatilities for Bolivia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

1000 800 600 400 200 0

ECO-R 80 70 60 50 40 30 20

FIN-R 80 60 40 20

2000

0

1500 1000 500

84

86

88

90 92 ECO-R

94 96 98 ECO-V

84

0

00

POL-V

500 400 300 200 100 0

FIN-V

POL-R 80 70 60 50 40 30

86

88

90

92

POL-R

94

96

98

00

84

86

88

90 92 FIN-R

94 96 98 FIN-V

00

COM-V

1000 800 600 400 200 0

84

COM-R 80 70 60 50 40 30 20

86

88

POL-V

90

92

COM-R

94

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .3 .2 .1 .0 −.1 −.2 −.3

.08 .06 .04 .02 .00

84

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

.010 .008 .006 .004 .002 .000

POL-R .15 .10 .05 .00 −.05 −.10

FIN-V

.5 .4 .3 .2 .1 .0

84

FIN-R

.4 .2 .0 −.2 −.4 −.6 −.8

86

88

90 92 FIN-R

94 96 98 FIN-V

00

COM-V

COM-R .10 .05 .00 −.05 −.10 −.15

.020 .015 .010 .005

84

86

88

90 92 POL-R

94 96 98 POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

278

S. Hoti and M. McAleer

Figure 4.66. Risk ratings, risk returns and volatilities for Brazil RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 90 80 70 60 50 40 30

70 60 800

50

600

40 30

400 200 0

84

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

120 100 80 60 40 20 0

84

POL-R 72 68 64 60 56 52

86

88

90

92

POL-R

94

96

98

00

500 400 300 200 100 0

84

86

88

90 92 FIN-R

94 96 98 FIN-V

00

COM-V

COM-R 80 70 60

250 200 150 100 50 0

50 40

84

86

88

POL-V

90

92

COM-R

94

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.3 .2 .1 .0 −.1 −.2 −.3

.1 .0

.04 .03 .02

−.1

.08

−.2

.06 .04

.01 .00

.02 84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .08

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .10 .05 .00 −.05 −.10 −.15

.04 .00 .006 .005 .004 .003 .002 .001 .000

−.04 −.08

.016 .012 .008 .004

84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 279

Figure 4.67. Risk ratings, risk returns and volatilities for Chile RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90 80 70 60 50 40

800 600 400 200 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

500 400 300 200 100 0

84

00

POL-R 90 80 70 60 50 40

86

88

90

92

POL-R

94

96

98

00

FIN-V

1000 800 600 400 200 0

84

FIN-R 90 80 70 60 50 40

86

88

90 92 FIN-R

94 96 FIN-V

98

COM-V

600 500 400 300 200 100 0

84

00

COM-R 90 80 70 60 50 40

86

88

POL-V

90

92

COM-R

94

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

.2

FIN-V

FIN-R

−.1

.03

−.2

.02 .01 .00

.00

.0

.04

84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

00

POL-R

.08

−.04 .006 .005 .004 .003 .002 .001 .000

−.08

84

86

88

90 92 FIN-R

94 96 FIN-V

98

COM-V

00

COM-R .08

.04

.04

.00 .005 .004 .003 .002 .001 .000

−.04 −.08

84

86

88

90 92 94 96 98 POL-R POL-V

00

.08 .04

.1

.00 .005 .004 .003 .002 .001 .000

−.04 −.08

84

86

88

90 92 COM-R

94 96 98 COM-V

00

280

S. Hoti and M. McAleer

Figure 4.68. Risk ratings, risk returns and volatilities for Colombia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 90

70 60 600 500 400 300 200 100 0

50 40

70 60

200

50

100

84

86

88

90 92 ECO-R

94 96 98 ECO-V

0

00

POL-R 70 65 60 55 50 45 40

POL-V

200 160 120 80 40 0

80 300

84

86

88

90 92 FIN-R

94 96 FIN-V

98

00

COM-R 72 68 64 60 56 52

COM-V

80 60 40 20

84

86

88

90

92

POL-R

94

96

98

0

00

84

86

88

POL-V

90

92

COM-R

94

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.1

.1

.0 .028 .024 .020 .016 .012 .008 .004 .000

−.1 −.2

84

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

POL-R

.3

.024 .020 .016 .012 .008 .004 .000

.0 −.1 −.2

84

86

88

90 92 FIN-R

94 96 FIN-V

98

00

COM-V

COM-R .10

.2 .0

.04

−.1

.03

−.2

.02

.00 .008

−.05

.006

−.10

.004 .002

.01 .00

.05

.1

.05

84

86

88

90 92 POL-R

94 96 98 POL-V

00

.2

.000

84

86

88

90 92 COM-R

94 96 98 COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 281

Figure 4.69. Risk ratings, risk returns and volatilities for Ecuador RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

280 240 200 160 120 80 40 0

84

ECO-R 80 70 60 50 40 30

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

POL-R 68

FIN-V

600 500 400 300 200 100 0

84

FIN-R 80 70 60 50 40 30 20

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 65 60

64

55

60 50 40 30 20 10 0

56 52

84

86

88

90

92

POL-R

94

96

98

00

100 80 60 40 20 0

50 45

84

86

88

POL-V

90

92

COM-R

94

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

.0 −.2

.02 .01 .00

84

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

POL-R .12 .08 .04 .00 −.04 −.08

.008 .006 .004 .002 .000

84

86

88

90 92 POL-R

94 96 98 POL-V

00

.2

.0 −.2

.03

FIN-R

.1 −.1

.04

FIN-V

.24 .20 .16 .12 .08 .04 .00

−.4 −.6

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

.010 .008 .006 .004 .002 .000

84

COM-R .08 .04 .00 −.04 −.08 −.12

86

88

90 92 94 96 98 COM-R COM-V

00

282

S. Hoti and M. McAleer

Figure 4.70. Risk ratings, risk returns and volatilities for Paraguay RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

500 400 300 200 100 0

84

ECO-R 90 80 70 60 50 40

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 80

POL-V

FIN-V

600 500 400 300 200 100 0

84

FIN-R 90 80 70 60 50 40

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 76 72 68 64 60 56 52 48

COM-V

70 60 50

200

40

150 100 50 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

250 200 150 100 50 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.3 .2 .1 .0 −.1 −.2 −.3

.1 .04

.0

.03

−.1

.02

−.2

.01 .00

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .10 .05 .00 −.05 −.10 −.15

.012 .008 .004 .000

84

86

88

90 92 94 96 98 POL-R POL-V

00

.06 .05 .04 .03 .02 .01 .00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .08 .04 .00

.006 .005 .004 .003 .002 .001 .000

−.04 −.08

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 283

Figure 4.71. Risk ratings, risk returns and volatilities for Peru RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

500 400 300 200 100 0

ECO-R 80 70 60 50 40 30

FIN-R 80 60 40

1200

20 0

800 400

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 80

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 80 70 60 50 40 30

70

600

60

500 400

50

300

40

200

30

100 0

FIN-V

84

86

88

90 92 94 96 98 POL-R POL-V

400 300 200 100 0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .3 .2

ECO-V

FIN-R

FIN-V

.4 .2 .0 −.2 −.4

.1 .06 .05 .04 .03 .02 .01 .00

.0 .15

−.2

.10 .05

84

86

88

90 92 94 96 98 ECO-R ECO-V

84

.00

00

POL-V

.06 .05 .04 .03 .02 .01 .00

−.1

88

90 92 94 96 98 POL-R POL-V

00

86

88

90

92

FIN-R POL-R .2 .1 .0 −.1 −.2 −.3

86

84

94

96

98

00

FIN-V

COM-V

.025 .020 .015 .010 .005 .000

84

COM-R .10 .05 .00 −.05 −.10 −.15

86

88

90 92 94 96 98 COM-R COM-V

00

284

S. Hoti and M. McAleer

Figure 4.72. Risk ratings, risk returns and volatilities for Uruguay RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 90 80

70 300 250 200 150 100 50 0

70

60 50

400

60

40

300

50

200 100 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 90

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80 75 70 65 60 55 50

COM-V

80 240 200 160 120 80 40 0

70 60 50

150 100 50

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .15 .10 .05 .00 −.05 −.10 −.15

.020 .016 .012 .008 .004 .000

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .08

FIN-V

FIN-R

.0 −.1 .06 .05 .04 .03 .02 .01 .00

−.2 −.3

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .08 .04 .00 −.04 −.08 −.12

.04 .00

.004

−.04

.003

−.08

.002

.008 .006 .004

.001

.002

.000

.000

84

86

88

90 92 94 96 98 POL-R POL-V

00

.1

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 285

Figure 4.73. Risk ratings, risk returns and volatilities for Venezuela RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90 80 70 60 50 40

400 300

FIN-V

800 600

200

400

100

200

0

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 80

FIN-R 90 80 70 60 50 40 30

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 76 72 68 64 60 56 52

70 60 280 240 200 160 120 80 40 0

50 40

160 120 80 40

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R .3 .2 .1 .0 −.1 −.2 −.3

.1 .0 −.1

.04

−.2

.03 .02 .01 .00

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

.025 .020 .015 .010 .005 .000

84

POL-R .12 .08 .04 .00 −.04 −.08 −.12 −.16

86

88

90 92 94 96 98 POL-R POL-V

00

.05 .04 .03 .02 .01 .00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00 COM-R .08 .04 .00 −.04 −.08 −.12

COM-V

.010 .008 .006 .004 .002 .000

84

86

88

90 92 94 96 98 COM-R COM-V

00

286

S. Hoti and M. McAleer

Figure 4.74. Risk ratings, risk returns and volatilities for Angola RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90 80 70 60 50 40 30

800 600 400

86

88

90

92 94 ECO-R

96 98 ECO-V

60 400

50

300

40

200

30

0

00

POL-V

120 100 80 60 40 20 0

FIN-R 70

100

200 0

FIN-V

POL-R 60 55 50 45 40 35

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R 56 52 48 44 40 36

80 60 40 20

86

88

90

92 94 POL-R

96 98 POL-V

0

00

86

88

90

92 94 COM-R

96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .3 .2 .1 .0 −.1 −.2 −.3

.08 .06

FIN-V

.15 .10

.02

.05 86

88

90

92 94 ECO-R

96 98 ECO-V

.00

00

POL-V

POL-R .2

.4 .2 .0 −.2 −.4 −.6

.20

.04 .00

FIN-R

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R .15 .10 .05 .00 −.05 −.10 −.15

.1 .04

.0

.03

−.1

.02

−.2

.01 .00

.015 .010 .005

86

88

90

92 94 POL-R

96 98 POL-V

00

.000

86

88

90

92 94 COM-R

96 98 COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 287

Figure 4.75. Risk ratings, risk returns and volatilities for Botswana RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 92 88 84 80 76 72

80 60 40 20 0

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 80

POL-V

FIN-V

500 400 300 200 100 0

FIN-R 100 90 80 70 60 50

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R 84 80 76 72 68 64

COM-V

76 72

80 60 40

68

80

64

60

60

40

20 0

20 86

88

90 92 94 96 98 POL-R POL-V

0

00

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .08

FIN-V

FIN-R

.12 .08 .04 .00 −.04 −.08 −.12

.04 .00 .006 .005 .004 .003 .002 .001 .000

−.04 −.08

.012 .008 .004

86

88

90 92 94 96 98 ECO-R ECO-V

.000

00

POL-R .08

POL-V

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R .06 .04 .02 .00 −.02 −.04 −.06

COM-V

.04 .005

.00

.004

−.04

.003

−.08

.002 .001 .000

86

88

90 92 94 96 98 POL-R POL-V

00

.0025 .0020 .0015 .0010 .0005 .0000

86

88

90 92 94 96 98 COM-R COM-V

00

288

S. Hoti and M. McAleer

Figure 4.76. Risk ratings, risk returns and volatilities for Burkina Faso RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 75

FIN-V

FIN-R 80 70

70 65 60

160

55

120 80 40 0

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-R 70

POL-V

60 200 160 120 80 40 0

50 40

600 500 400 300 200 100 0

60 50 40 30

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R 68

COM-V

64 160

60

120

56 52

80

48

40 86

88

90

92 94 96 98 POL-R POL-V

0

00

86

88

90

92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .15 .10 .05 .00 −.05 −.10 −.15

.025 .020 .015 .010

FIN-V

.06 .04 .02

.000

.00

88

90

92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .2

.3 .2 .1 .0 −.1 −.2 −.3

.08

.005 86

FIN-R

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .12 .08

.1 .04

.0

.03

−.1

.02

−.04

.008

−.08

.004

.01 .00

.04 .00

.012

86

88

90

92 94 96 98 POL-R POL-V

00

.000

86

88

90

92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 289

Figure 4.77. Risk ratings, risk returns and volatilities for Cameroon RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 70 65 60 55 50 45

70 60 500 400 300 200 100 0

50 40

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 60 56 52 48 44 40

POL-V

60 50 40 30 20 10 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

100 80 60 40 20 0

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 65

COM-V

60 55 100 80 60 40 20 0

50 45

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .4

.12 .08 .04 .00 −.04 −.08 −.12

.0

.08 .06

−.2 −.4

.04 .02 84

86

88

90 92 94 96 98 ECO-R ECO-V

.015 .010 .005 .000

00

POL-V

.012 .010 .008 .006 .004 .002 .000

FIN-R

.2

.12 .10

.00

FIN-V

POL-R .10 .05 .00 −.05 −.10 −.15

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .08 .04

.008

.00

.006

−.04

.004

−.08

.002 84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

290

S. Hoti and M. McAleer

Figure 4.78. Risk ratings, risk returns and volatilities for Congo RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80 70 60 50 40 30

800 600

60 400

50

300

40 30

100

200 86

88

90

92 94 96 98 ECO-R ECO-V

0

00

POL-V

250 200 150 100 50 0

FIN-R 70

200

400 0

FIN-V

POL-R 60 55 50 45 40 35

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 65 60 55 50 45 40

160 120 80 40

86

88

90

92 94 96 98 POL-R POL-V

0

00

86

88

90

92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .8

ECO-V

FIN-R

FIN-V

.4

.0

.3

−.4

.2

−.8

.1 .0

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-V

.12 .10 .08 .06 .04 .02 .00

86

POL-R .2 .1 .0 −.1 −.2 −.3 −.4

88

90

92 94 96 98 POL-R POL-V

00

.4 .2

.4

.0 .10 .08 .06 .04 .02 .00

−.2 −.4

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R .2 .1 .0

.025 .020 .015 .010 .005 .000

−.1 −.2

86

88

90

92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 291

Figure 4.79. Risk ratings, risk returns and volatilities for Coˆte d’Ivoire RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 70 65 60 55 50 45

70 60 500 400 300 200 100 0

50 40

100 50 88

90

92 94 ECO-R

96 98 ECO-V

0

00

POL-V

POL-R 70 65 60 55 50 45 40

400 300 200 100 0

150

88

90

92 94 POL-R

96 98 POL-V

00

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R 70 65 60

120 100 80 60 40 20 0

55 50

88

90

92 94 COM-R

96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .2 .1 .0 −.1 −.2 −.3 −.4

ECO-V

.12 .08

.0

.020

−.1

.015

−.2

.005 88

90

92 94 ECO-R

96 98 ECO-V

.000

00

POL-V

POL-R .2 .1 .0 −.1 −.2 −.3 −.4

.16 .12 .08 .04 .00

.2 .1

.025

.010

.04 .00

FIN-R

FIN-V

88

90

92 94 POL-R

96 98 POL-V

00

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R .1 .0 −.1

.06 .05 .04 .03 .02 .01 .00

−.2 −.3

88

90

92 94 COM-R

96 98 COM-V

00

292

S. Hoti and M. McAleer

Figure 4.80. Risk ratings, risk returns and volatilities for DR Congo RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

1000 800 600 400 200 0

84

ECO-R 70 60 50 40 30 20 10

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

POL-R 50

FIN-V

1200 1000 800 600 400 200 0

84

FIN-R 80 70 60 50 40 30 20

86

88

90 92 FIN-R

94 96 FIN-V

98

00

COM-V

COM-R 55 50 45 40 35 30 25

40 250 200 150 100 50 0

30

250

20

200

10

150 100 50

84

86

88

90 92 POL-R

94 96 98 POL-V

0

00

84

86

88

90 92 COM-R

94 96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R 1.0 0.5 0.0 −0.5 −1.0 −1.5

ECO-V

1.5 1.0

FIN-R .4 .2 .0 −.2 −.4

FIN-V

.15 .10

0.5

.05

0.0

.00

84

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

POL-R .8

84

86

88

90 92 FIN-R

94 96 FIN-V

98

00

COM-V

COM-R .3 .2 .1 .0 −.1 −.2 −.3

.4 .0 .6 .5 .4 .3 .2 .1 .0

−.4 −.8

84

86

88

90 92 POL-R

94 96 98 POL-V

00

.10 .08 .06 .04 .02 .00

84

86

88

90 92 COM-R

94 96 98 COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 293

Figure 4.81. Risk ratings, risk returns and volatilities for Ethiopia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80 70 60 50 40 30 20

800 600

FIN-V

1000 800 600

400

400

200

200

0

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 70 60 50 40 30 20 10

POL-V

800 600 400 200 0

86

88

90

92

POL-R

94

96

98

00

FIN-R 80 70 60 50 40 30 20

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

600 500 400 300 200 100 0

86

COM-R 80 70 60 50 40 30 20

88

POL-V

90

92

COM-R

94

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

.4

FIN-R

FIN-V

.4 .2 .0 −.2 −.4 −.6

.2 .0 .12 .10 .08 .06 .04 .02 .00

−.2 −.4

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .6

.20 .16 .12 .08 .04 .00

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .2

.4 .2

.25

.0

.20

−.2

.15

−.4

.10

.0 −.1

.02

−.2 .01

.05 .00

.1 .03

86

88

90 92 94 96 98 POL-R POL-V

00

.00

86

88

90 92 94 96 98 COM-R COM-V

00

294

S. Hoti and M. McAleer

Figure 4.82. Risk ratings, risk returns and volatilities for Gabon RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 90

FIN-V

FIN-R 76 72 68 64 60 56

80 70 400 300

60

80

50

60

200

40

100

20

0

84

86

88

90 92 ECO-R

94 96 98 ECO-V

0

00

POL-V

POL-R 68

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 72

64 50

60

40

56

30

52

20

40

64

30

60

20 10

10 0

68

84

86

88

90 92 POL-R

94 96 98 POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.06 .05 .04 .03 .02 .01 .00

84

ECO-R .2 .1 .0 −.1 −.2 −.3

86

88

90 92 ECO-R

94 96 98 ECO-V

00

POL-V

POL-R .12

FIN-V

.012 .010 .008 .006 .004 .002 .000

84

FIN-R

.10 .05 .00 −.05 −.10 −.15

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .08

.08 .012 .010 .008 .006 .004 .002 .000

.04

.04

.00

.00

.004

−.04

−.04

.003

−.08

−.08

.002 .001

84

86

88

90 92 POL-R

94 96 98 POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 295

Figure 4.83. Risk ratings, risk returns and volatilities for Ghana RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 70

FIN-V

FIN-R 70

60

60

50 600 500 400 300 200 100 0

40 30

50 400

40

300

30

200 100 84

86

88

90 92 ECO-R

94 96 98 ECO-V

0

00

POL-R 70

POL-V

84

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R 70

COM-V

60

60

50 500 400 300 200 100 0

40 30

84

86

88

90 92 POL-R

94 96 98 POL-V

00

50 500 400 300 200 100 0

40 30

84

86

88

90

92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .3

FIN-V

FIN-R

.1

.08

.0

.06

−.1

.04

−.2

.02 .00

.1

.05

.0

.04

−.1

.03

−.2

.02 .01

84

86

88

90 92 ECO-R

94 96 98 ECO-V

.00

00

POL-V

POL-R .15

84

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .10

.10 .012 .010 .008 .006 .004 .002 .000

.3 .2

.2

.05

.05 .00 −.05 −.10

.008

.00

.006

−.05

.004 .002

84

86

88

90 92 POL-R

94 96 98 POL-V

00

.000

84

86

88

90

92 94 96 98 COM-R COM-V

00

296

S. Hoti and M. McAleer

Figure 4.84. Risk ratings, risk returns and volatilities for Guinea RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

250 200 150 100 50 0

ECO-R 70 65 60 55 50 45 40

FIN-R 90 80

1000

70

800

60

600

50

400

40 30

200 86

88

90

92 94 96 98 ECO-R ECO-V

0

00

POL-R 60

POL-V

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R 64

COM-V

60

56

60 50 40 30 20 10 0

FIN-V

86

88

90

92 POL-R

94

96

98

52

120

48

80

44

40 0

00

56 52 48 44

86

88

90

POL-V

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.0 .04

.05

−.1

.03

.04

−.2

.03

.02

.02

.01

.01

.00

86

88

90

92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .12

.1 .0 −.1 −.2

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .08

.08

.04

.04

.010

.00

.008

−.04

.006

−.08

.004

.00

.002 .001 .000

90

92 94 96 98 POL-R POL-V

00

−.08

.003

.000

88

−.04

.004

.002 86

.3 .2

.1

86

88

90

92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 297

Figure 4.85. Risk ratings, risk returns and volatilities for Kenya RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 75 70 65 60 55 50 45

150

FIN-V

150

100

100

50

50

0

84

86

88

90 92 ECO-R

94 96 98 ECO-V

0

00

POL-R 70

POL-V

FIN-R 75 70 65 60 55 50 45

84

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-R 70

COM-V

65 60 160

55

120

50 45

80

60 55 50

80

45 40

40 0

65 120

84

86

88

90 92 POL-R

94 96 98 POL-V

0

00

84

86

88

90

92 94 COM-R

96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .2

FIN-V

FIN-R

.10 .05 .00 −.05 −.10 −.15

.1 .0 −.1

.03

−.2

.02

.015 .010

.01 .00

.020

.005 84

86

88

90 92 ECO-R

94 96 98 ECO-V

.000

00

POL-V

POL-R .15

84

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R .08

.10

.04

.05 .020

.00

.015

−.05 −.10

.010 .005 .000

.00 .008

−.04

.006

−.08

.004 .002

84

86

88

90

92

POL-R

94

96

POL-V

98

00

.000

84

86

88

90

92

94

COM-R

96

98

COM-V

00

298

S. Hoti and M. McAleer

Figure 4.86. Risk ratings, risk returns and volatilities for Liberia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80

ECO-V

FIN-R 45 40 35 30 25 20 15

FIN-V

60 1000 800 600 400 200 0

250

20

200

0

150 100 50

84

86

88

90 92 94 96 98 ECO-R ECO-V

84

0

00

POL-V

600 500 400 300 200 100 0

40

POL-R 60 50 40 30 20 10 0

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

500 400 300 200 100 0

84

COM-R 60 50 40 30 20 10

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .8

FIN-V

FIN-R

.4 .0 −.4

.3

−.8

.4 .2 84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

1.0 0.8 0.6 0.4 0.2 0.0

.0

00

POL-R

1.5 1.0 0.5 0.0 −0.5 −1.0

.0

.6

.1 .0

.4

.8

.2

−.4

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .4 .2 .0

.16

−.2

.12

−.4

.08 .04 84

86

88

90 92 94 96 98 POL-R POL-V

00

.8

.00

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 299

Figure 4.87. Risk ratings, risk returns and volatilities for Malawi RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 65 60 55 50 45 40

160 120 80 40 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 80

POL-V 300

70 60

200 100 0

84

86

88

90 92 POL-R

94

96 98 POL-V

FIN-V

250 200 150 100 50 0

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 68

COM-V 160

64

120

60

50

80

40

40 0

00

84

FIN-R 65 60 55 50 45 40 35

56 52 48 84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .4 .3 .2 .1 .0 −.1 −.2

ECO-V

.10 .08 .06 .04 .02 .00

FIN-R

FIN-V

.2 .1

.10

.0

.08

−.1

.06

−.2

.04 .02 84

86

88

90 92 ECO-R

94

96 98 ECO-V

.00

00

POL-V

POL-R .15

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .10

.10

.05

.05

.020

.00

.016

−.05

.012

−.10

.008 .004 .000

.3

84

86

88

90 92 POL-R

94

96 98 POL-V

00

.010 .008 .006 .004 .002 .000

.00 −.05

84

86

88

90 92 94 96 98 COM-R COM-V

00

300

S. Hoti and M. McAleer

Figure 4.88. Risk ratings, risk returns and volatilities for Mali RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80

ECO-V

FIN-R 80 70 60 50 40 30

FIN-V

70 60

400

50

300

40

200 100 0

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R 70 60 50

300 200

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 70 60

400

40

300

30

200

100 0

1000 800 600 400 200 0

50 40 30

100 86

88

90 92 94 96 98 POL-R POL-V

0

00

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .4

FIN-V

FIN-R

.2

0.0

.0 .10 .08 .06 .04 .02 .00

−.2 −.4

−0.5

.8

−1.0

.6 .4 .2

86

88

90 92 94 96 98 ECO-R ECO-V

.0

00

POL-R .3

POL-V

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R .2 .1 .0 −.1 −.2 −.3

COM-V

.2 .1

.08

.0

.06

−.1

.04 .02 .00

0.5

86

88

90 92 94 96 98 POL-R POL-V

00

.06 .05 .04 .03 .02 .01 .00

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 301

Figure 4.89. Risk ratings, risk returns and volatilities for Mozambique RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 60 50 40 30 20 10 0

800 600 400

FIN-R 65 60

160

55

120

50

80

45 40

40

200 0

FIN-V

86

88

90

92 94 ECO-R

96 98 ECO-V

0

00

POL-R 70

POL-V

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-R 70

COM-V

60 50

200 160 120

40 30

50

250 200

40

150

30

100

80 40 0

60

50 86

88

90

92 94 POL-R

96 98 POL-V

0

00

86

88

90

92 94 COM-R

96 98 COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .8

FIN-V

FIN-R

.6 .5 .4 .3 .2 .1 .0

.0

.1 .0

.08

−.4

.06

−.8

.04

−.1 −.2

.02 86

88

90

92 94 ECO-R

96 98 ECO-V

.00

00

POL-V

POL-R .16 .12 .08 .04 .00 −.04 −.08

.025 .020 .015 .010 .005 .000

.3 .2

.4

86

88

90

92 94 FIN-R

96 98 FIN-V

00

COM-V

COM-R .15 .10 .05

.025

.00

.020

−.05

.015

−.10

.010 .005 86

88

90 92 POL-R

94

96 98 POL-V

00

.000

86

88

90

92 94 COM-R

96 98 COM-V

00

302

S. Hoti and M. McAleer

Figure 4.90. Risk ratings, risk returns and volatilities for Nigeria RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80

FIN-V

FIN-R 100 80

70 400

60

300

50

200

40

100 0

40

800

20

600 400 200

84

86

88

90 92 94 96 98 ECO-R ECO-V

84

0

00

POL-R 60 55 50 45 40 35

POL-V

120 100 80 60 40 20 0

60

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 65 60 55 50 45 40 35

COM-V

200 160 120 80 40 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .4

FIN-V

FIN-R

.3 .2 .1 .0 −.1 −.2 −.3

.2 .12 .10 .08 .06 .04 .02 .00

.0 −.2 −.4

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .15

.10 .08 .06 .04 .02 .00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .15 .10 .05 .00 −.05 −.10 −.15

.10 .05

.020

.00

.016

−.05

.012

−.10

.008 .004 .000

.020 .016 .012 .008 .004

84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 303

Figure 4.91. Risk ratings, risk returns and volatilities for Senegal RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 80 70

FIN-V

FIN-R 75

200

70

160

65

120

60

80

55

100

40

50

0

0

60 400

50

300

40

200

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 64

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 68

COM-V

60 56 30

52

20

48

10 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

64 60 50 40 30 20 10 0

60 56 52

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.10 .08 .06 .04 .02 .00

ECO-R .2 .1 .0 −.1 −.2 −.3 −.4

FIN-V

FIN-R

.10 .05

.020

.00

.016

−.05

.012

−.10

.008 .004 84

86

88

90 92 94 96 98 ECO-R ECO-V

.000

00

POL-V

POL-R .12

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .05

.08

.00

.04

.010

.00

.008

−.04

.006

−.08

.004

.008

−.05

.006

−.10

.004 .002

.002 .000

.15

84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

304

S. Hoti and M. McAleer

Figure 4.92. Risk ratings, risk returns and volatilities for Sierra Leone RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 70 60 50 40 30 20 10

800 600 400 200 0

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-V

250 200 150 100 50 0

POL-R 52 48 44 40 36 32 28 24

FIN-V

500 400 300 200 100 0

86

FIN-R 60 50 40 30 20 10

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 50 45 40 35 30 25

200 150 100 50

86

88

90

92 94 96 98 POL-R POL-V

0

00

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 1.2 0.8 0.4 0.0 −0.4 −0.8

.8 .6 .4 .2 .0

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-V

.10 .08 .06 .04 .02 .00

POL-R .3 .2 .1 .0 −.1 −.2 −.3

FIN-V

FIN-R

.4 .0 .6 .5 .4 .3 .2 .1 .0

−.4 −.8

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .2 .1 .0

.04

−.1

.03

−.2

.02 .01 86

88

90

92 94 96 98 POL-R POL-V

00

.8

.00

86

88

90

92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 305

Figure 4.93. Risk ratings, risk returns and volatilities for South Africa RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80 75 70 65 60 55

ECO-V

100 80 60 40 20 0

FIN-R 90 80 70 60 50 40

FIN-V

800 600 400 200

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 80

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80 75 70 65 60 55 50

COM-V

70 60 250 200 150 100 50 0

50 40

84

86

88

90 92 94 96 98 POL-R POL-V

00

240 200 160 120 80 40 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .10 .05 .00 −.05 −.10 −.15

ECO-V

.020 .016 .012 .008 .004 .000

.025

.0

.020

−.1

.015

−.2

.005 84

86

88

90 92 94 96 98 ECO-R ECO-V

.000

00

POL-R .20 .15 .10 .05 .00 −.05 −.10

.03 .02 .01

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .12

COM-V

.08 .04

.008

.00

.006

−.04

.004 .002

84

86

88

90 92 94 96 98 POL-R POL-V

00

.2 .1

.030

.010

POL-V

.00

FIN-R

FIN-V

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

306

S. Hoti and M. McAleer

Figure 4.94. Risk ratings, risk returns and volatilities for Sudan RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 80 70 60 50 40 30 20

ECO-V

800 600

60

1000

50

800

40

600

30

400

400

20

200

200 0

FIN-R 70

FIN-V 1200

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 50

POL-V

10 84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 60

COM-V

40

250 200 150 100 50 0

50

30

500

40

20

400

10

30

300

20

200

10

100 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .6 .4 .2 .0 −.2 −.4 −.6

.4 .3 .2 .1 .0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .6

FIN-V

FIN-R

.4 .2 .28 .24 .20 .16 .12 .08 .04 .00

.0 −.2 −.4

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .3 .2

.4 .2 .28 .24 .20 .16 .12 .08 .04 .00

.0 −.2

.1 .08

.0

.06

−.1 −.2

.04 .02 84

86

88

90 92 94 96 98 POL-R POL-V

00

.6

.00

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 307

Figure 4.95. Risk ratings, risk returns and volatilities for Tanzania RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 70 60 50 40 30 20

800 600 400 200 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 70 65 60 55 50 45

POL-V

120 80

600 500 400 300 200 100 0

84

FIN-R 80 70 60 50 40 30 20

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 68 64 60 56 52 48 44

COM-V

160 120 80

40 0

FIN-V

40 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .8 .6 .4 .2 .0 −.2 −.4

.4 .3

FIN-V

.06 .04

.1

.02 84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .12

.3 .2 .1 .0 −.1 −.2 −.3

.08

.2 .0

FIN-R

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .12 .08

.08 .04

.010

.00

.008 .006 .004

.00

−.04

.012

−.04

−.08

.008

−.08

.004

.002 .000

.04

.016

84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

308

S. Hoti and M. McAleer

Figure 4.96. Risk ratings, risk returns and volatilities for Togo RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

600 500 400 300 200 100 0

ECO-R 80 70 60 50 40 30

FIN-R 75 70

250

65

200

60

150

55 50

100

45

50 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

200 160 120 80 40 0

FIN-V

POL-R 60 55 50 45 40 35 30

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 65 60 55 50 45 40

120 80 40

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .4

FIN-V

FIN-R

.10

.0

.08

−.2

.06

−.4

.04

84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

.012

−.04

.008

.000

00

POL-R .3 .2 .1 .0 −.1 −.2

POL-V

.08 .06 .04

−.3

.02 .00

.04

.016

−.08

.004

.02 .00

.12 .08

.2

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .08 .04 .00 −.04 −.08 −.12

COM-V

.008 .006 .004 .002

84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 309

Figure 4.97. Risk ratings, risk returns and volatilities for Uganda RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 100 80 60 40 20 0

ECO-V

1600 1200

70

800

60

600

50 40 30

200

400 84

86

88

90 92 94 96 98 ECO-R ECO-V

84

0

00

POL-R 70 60 50 40 30 20

POL-V

500 400 300 200 100 0

80 1000

400

800 0

FIN-R 90

FIN-V

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 70 60 50 40 30 20

COM-V

500 400 300 200 100 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .8

ECO-V

FIN-R

FIN-V

.3 .2 .1 .0 −.1 −.2

.4 .0 −.4

.8

−.8

.6 .4 .2 .0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .15

.05 .04 .03 .02 .01 .00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .2

.10 .05 .00

.016 .012 .008

−.05

.03

−.10

.02

.0 −.1 −.2

.01

.004 .000

.1 .04

84

86

88

90 92 94 96 98 POL-R POL-V

00

.00

84

86

88

90 92 94 96 98 COM-R COM-V

00

310

S. Hoti and M. McAleer

Figure 4.98. Risk ratings, risk returns and volatilities for Zambia RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 70

ECO-V

FIN-R 60

FIN-V

60

50

50 40

400

30

300

20

200

30

200

20

150 100

100 0

40

250

50 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 80

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 70

COM-V

60

70 60

300

50

200

40

100 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

50 250 200 150 100 50 0

40 30

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .4

FIN-V

FIN-R

.3 .2 .1 .0 −.1 −.2 −.3

.2 .0 .16

−.2

.12

−.4

.08 .06

.08

.04

.04

.02

.00

84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

.10 .08 .06 .04 .02 .00

.00

00

POL-R .4 .3 .2 .1 .0 −.1

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .2 .1

.04 .0

.03

−.1

.02 .01 84

86

88

90 92 94 96 98 POL-R POL-V

00

.00

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 311

Figure 4.99. Risk ratings, risk returns and volatilities for Zimbabwe RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 70 60 50 40 30 20

ECO-V

1000 800 600 400 200 0

FIN-R

FIN-V

65

250

60

200

55

150

50

100

45 40

50 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 70

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 70

COM-V

60

60

50 40

400

30

300 200 100 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

70

50 250 200 150 100 50 0

40 30

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.3 .2 .1 .0 −.1 −.2 −.3

.08 .06

.1 .08

.0

.06

−.1

.04

.02

.02 84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

.00

00

POL-R

.2

−.2

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-V

00

COM-R .10 .05

.1

.03 .02

−.1

.008

−.2

.006

−.05 −.10

.004

.01 .00

.00

.0

.04

.002 84

86

88

90 92 94 96 98 POL-R POL-V

00

.3 .2

.04 .00

FIN-R

FIN-V

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

312

S. Hoti and M. McAleer

Figure 4.100. Risk ratings, risk returns and volatilities for Austria RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 88

ECO-V

FIN-R 100 95 90 85 80 75

FIN-V

84 50 40 30 20 10 0

80

160

76

120 80 40

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 96 92 88 84 80 76

POL-V

80 60 40 20 0

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 90 88 86 84 82 80

COM-V

30 25 20 15 10 5 0

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .10

ECO-V

FIN-R

FIN-V

.00

.00

−.05

−.05 .010 .008 .006 .004 .002 .000

.05

.05

−.10

−.10

.020

−.15

.015 .010 .005 84

86

88

90 92 94 96 98 ECO-R ECO-V

.000

00

POL-R .08 .04 .00 −.04 −.08 −.12

POL-V

.008 .006 .004

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .04 .02 .00 −.02 −.04 −.06

COM-V

.004 .003 .002

.002

.001

.000

.000

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 313

Figure 4.101. Risk ratings, risk returns and volatilities for Belgium RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 85 80 75 70 65

ECO-V

120 100 80 60 40 20 0

90 80

400

70

300

60

200 100 84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 88

POL-V

84 60 50 40 30 20 10 0

FIN-R 100

FIN-V

80

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 88

COM-V

86 84

40

76

30

72

20

82 80 78 76

10 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .08

ECO-V

.0

.00

−.1

−.08

.003 .002 .001 .000

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R

POL-V

.04

.06 .05 .04 .03 .02 .01 .00

−.2 −.3

84

86

88

90 92 94 96 98 FIN-R FIN-V

.00 −.02 −.04

.0012 .0008 .0004 .0000

84

86

88

90 92 94 96 98 POL-R POL-V

00

.005 .004 .003 .002 .001 .000

84

00

COM-R .04 .02 .00 −.02 −.04 −.06 −.08

COM-V

.02

.0016

.1

.04 −.04

.004

FIN-R

FIN-V

86

88

90 92 94 96 98 COM-R COM-V

00

314

S. Hoti and M. McAleer

Figure 4.102. Risk ratings, risk returns and volatilities for Cyprus RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 88 84 80 76 72 68

ECO-V

100 80 60 40 20 0

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-R 90 80 70 60 50 40

POL-V

400 300 200

250 200 150 100 50 0

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 85 80 75 70 65 60 55

200 150 100

100 0

FIN-R 95 90 85 80 75 70 65

FIN-V

50 86

88

90

92 94 96 98 POL-R POL-V

0

00

86

88

90

92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .15 .10 .05 .00 −.05 −.10

ECO-V

.012 .008 .004 .000

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-R .12

POL-V

FIN-R

FIN-V

.1 .0 .025 .020 .015 .010 .005 .000

−.1 −.2

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .08

.08 .04 .008

.00

.006

−.04 −.08

.004

.000

.04

.008

.00

.006

−.04

.004

−.08

.002

.002 86

88

90

92 94 96 98 POL-R POL-V

00

.2

.000

86

88

90

92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 315

Figure 4.103. Risk ratings, risk returns and volatilities for Denmark RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

100 80 60 40 20 0

ECO-R 90 85 80 75 70 65

FIN-V

FIN-R 100 90 80 70

300

60

200 100

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-V

POL-R 96

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 92 90 88 86 84 82 80

92 88 50 40 30 20 10 0

84

30

80

20 10

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90

92

COM-R

94

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .08

FIN-V

FIN-R .2 .1

.04

.0

.00 .004

−.04

.003

−.08

.002

−.2

.04 .03 .02

.001 .000

−.1

.01 84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .04

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .06 .04 .02 .00 −.02 −.04 −.06

.02 .00 −.02 .0015

−.04

.0010

.003 .002

.0005 .0000

.004

.001 84

86

88

90 92 94 96 98 POL-R POL-V

00

.000

84

86

88

90 92 94 96 98 COM-R COM-V

00

316

S. Hoti and M. McAleer

Figure 4.104. Risk ratings, risk returns and volatilities for Finland RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 100

FIN-V

FIN-R 90 85 80 75 70 65

90 80 400

70

300

60

300

50

200

200

100

100 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

60 50 40 30 20 10 0

0

00

POL-R 100 96 92 88 84 80

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 92 88 84

40

80

30

76

20 10 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .15 .10 .05 .00 −.05 −.10

.015 .010

FIN-R .10 .05 .00 −.05 −.10 −.15 −.20

.04 .03 .02

.005 .000

FIN-V

.01 84

86

88

90 92 94 96 98 ECO-R ECO-V

.00

00

POL-V

POL-R .06

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .04

.04 .0025

.00

.0020

−.02

.0015

−.04

.0010 .0005 .0000

.02

.02

84

86

88

90 92 94 96 98 POL-R POL-V

00

.00 .0012 .0010 .0008 .0006 .0004 .0002 .0000

−.02 −.04

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 317

Figure 4.105. Risk ratings, risk returns and volatilities for France RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 85 80 75 70 65

ECO-V

160 120 80 40 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

00

POL-R 88

FIN-R 100

FIN-V

90 80 250 200 150 100 50 0

70 60

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-V

00

C

80 40

76

30

72

20

82

20

80

15

78

10

76

5

10 0

86 84

84

84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

.08

FIN-V

FIN-R

.04

.0

.00 −.04

.003

−.08

.002

−.2

.04 .03 .01

84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

POL-R

.12 .08 .04 .00 −.04 −.08

.006 .004 .002 84

.00

00

.008

.000

−.1

.02

.001 .000

.1

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-V

.0025 .0020 .0015 .0010 .0005 .0000

84

86

00

COM-R .04 .02 .00 −.02 −.04 −.06

88

90 92 94 96 98 COM-R COM-V

00

318

S. Hoti and M. McAleer

Figure 4.106. Risk ratings, risk returns and volatilities for Germany RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90 85 80 75 70 65 60

ECO-V

250 200 150 100 50 0

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R 92 88 84 80 76 72

80 60 40 20 0

86

88

90

92 94 96 98 POL-R POL-V

00

FIN-R

FIN-V

100 90 300 250 200 150 100 50 0

80 70

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 88 84 80

50 40 30 20 10 0

76 72

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .3 .2 .1 .0

.04

−.1

.03

−.2

.02 .01 .00

86

88

90

92 94 96 98 ECO-R ECO-V

00

POL-R .06

POL-V

FIN-V

.05 .04 .03 .02 .01 .00

86

FIN-R

.2 .1 .0 −.1 −.2 −.3

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R .12

COM-V

.04

.08

.02 .0025

.00

.0020

−.02

.0015

−.04

.0010

.00 −.04

.010

−.08

.005

.0005 .0000

.04 .015

86

88

90

92 94 96 98 POL-R POL-V

00

.000

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 319

Figure 4.107. Risk ratings, risk returns and volatilities for Greece RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 90

ECO-V

FIN-R 80

FIN-V

80

70

70 250 200 150 100 50 0

60 50

50 40

200 100

84

86

88

90 92 94 96 98 ECO-R ECO-V

0

00

POL-R 90

POL-V

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 80 75 70 65 60 55

COM-V

80

250 200

70

150

60

100

50

50 0

60 300

84

86

88

90 92 94 96 98 POL-R POL-V

150 100 50 0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .2

ECO-V

FIN-R

FIN-V

.1

.1

.0 .04

−.1

.03

−.2

.02 .01 .00

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R .08 .06 .04 .02 .00 −.02 −.04

POL-V

.004 .003 .002

.0 .030 .025 .020 .015 .010 .005 .000

.001 90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R .06 .04 .02 .00 −.02 −.04

.002

.000

88

−.2

.003

.000

86

−.1

COM-V

.001 84

.2

84

86

88

90 92 94 96 98 COM-R COM-V

00

320

S. Hoti and M. McAleer

Figure 4.108. Risk ratings, risk returns and volatilities for Iceland RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

500 400 300 200 100 0

ECO-R

FIN-V

FIN-R 90 80 70

800

60

600

50

400 200 84

86

88

90 92 94 96 98 ECO-R ECO-V

84

0

00

POL-V

60 50 40 30 20 10 0

90 80 70 60 50 40

POL-R 96 92 88 84 80 76

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

30 25 20 15 10 5 0

84

86

COM-R 86 84 82 80 78 76 74

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R .15 .10 .05 .00 −.05 −.10

.016 .012 .008 .004 .000

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .06

FIN-V

.012 .010 .008 .006 .004 .002 .000

84

FIN-R .08 .04 .00 −.04 −.08 −.12

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R .03 .02 .01 .00 −.01 −.02 −.03

.04 .02

.0025

.00

.0020

−.02

.0015

−.04

.0010

.0008 .0006 .0004

.0005

.0002

.0000

.0000

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 321

Figure 4.109. Risk ratings, risk returns and volatilities for Ireland RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 95

ECO-V

FIN-R 92 88 84 80 76 72

FIN-V

90 150

85 80

100

75 70

50 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R 95 90 85 80 75 70

POL-V

120 100 80 60 40 20 0

120 100 80 60 40 20 0

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-R 92

COM-V

88 80

84

60

80 76

40

72

20 84

86

88

90 92 94 96 98 POL-R POL-V

0

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .12

ECO-V

FIN-R

FIN-V

.08 .04 .00 −.04 −.08 −.12

.08 .04 .010

.00

.008

−.04

.006

−.08

.004 .002 .000

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R .06 .04 .02 .00 −.02 −.04 −.06

.004 .003 .002 .001 .000

84

86

88

90 92 94 96 98 POL-R POL-V

00

.010 .008 .006 .004 .002 .000

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

.0010 .0008 .0006 .0004 .0002 .0000

84

COM-R .03 .02 .01 .00 −.01 −.02 −.03

86

88

90 92 COM-R

94 96 98 COM-V

00

322

S. Hoti and M. McAleer

Figure 4.110. Risk ratings, risk returns and volatilities for Italy RIS KRATINGS AND ASSOCIATED VOLATILITIES ECO-V

120 100 80 60 40 20 0

ECO-R 90 85 80 75 70 65

FIN-R

200 150

95 90 85 80 75 70

100 50 84

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

120 100 80 60 40 20 0

FIN-V

84

0

00

POL-R 90 85 80 75 70 65

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-V

00

COM-R 85 80 75

60 50 40 30 20 10 0

70

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.010 .008 .006 .004 .002 .000

84

ECO-R

.15 .10 .05 .00 −.05 −.10

86

88

90 92 94 96 98 ECO-R ECO-V

POL-V

00

POL-R

.12

FIN-V

FIN-R

.04 .00 −.04 .006 .005 .004 .003 .002 .001 .000

−.08

84

86

88

90 92 94 96 98 FIN-R FIN-V

COM-V

00

COM-R .06 .04

.08 .00

.006

−.04

.004

−.08

.002 .000

.02

.04

.008

84

86

88

90 92 94 96 98 POL-R POL-V

00

.08

.0024 .0020 .0016 .0012 .0008 .0004 .0000

.00 −.02 −.04

84

86

88

90 92 94 96 98 COM-R COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 323

Figure 4.111. Risk ratings, risk returns and volatilities for Luxembourg RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R 100

ECO-V

FIN-R 100 95 90 85 80 75

FIN-V

90 250

80

200

70

150

60

100

86

88

90

92 94 96 98 ECO-R ECO-V

200 0

00

POL-R 96 94 92 90 88 86

POL-V

20 15 10 5 0

300 100

50 0

400

86

88

90

92 94 96 98 POL-R POL-V

00

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R 96 94 92 90 88 86

COM-V

20 16 12 8 4 0

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R .2

ECO-V

FIN-R .08 .04 .00 −.04 −.08 −.12

FIN-V

.1 .0 .025 .020 .015 .010 .005 .000

−.1 −.2

.012 .008 .004

86

88

90

92 94 96 98 ECO-R ECO-V

.000

00

POL-R .04

POL-V

86

88

90

92 94 96 98 FIN-R FIN-V

00

COM-R .04

COM-V

.02

.02

.00 −.02

.0012

−.04

.0008

−.02

.0012

−.04

.0008

.0004 .0000

.00 .0016

.0004 86

88

90

92 94 96 98 POL-R POL-V

00

.0000

86

88

90 92 94 96 98 COM-R COM-V

00

324

S. Hoti and M. McAleer

Figure 4.112. Risk ratings, risk returns and volatilities for Malta RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

300

92 88 84 80 76 72 68 64

200 100 0

1986 1988 1990 1992 1994 1996 1998 2000 ECO-R

80 250 200 150 100 50 0 1986 1988 1990 1992 1994 1996 1998 2000 FIN-R

POL-R

COM-R

COM-V

90 70

300

60

160

50

120

200

80

100

40

0

0

1986 1988 1990 1992 1994 1996 1998 2000 POL-R

70 60

FIN-V

80 400

100 90

ECO-V

POL-V

FIN-R

FIN-V

90 85 80 75 70 65 60

1986 1988 1990 1992 1994 1996 1998 2000 COM-R

POL-V

COM-V

RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

FIN-R

FIN-V

.2 .1 .0 −.1

.03

−.2

.02

.2 .1

.05

.0

.04

−.1

.03

−.2

.02

.01

.3

.01 .00 1986 1988 1990 1992 1994 1996 1998 2000

.00 1986 1988 1990 1992 1994 1996 1998 2000 ECO-R

ECO-V

FIN-R POL-R

POL-V

.03 .02

.20 .15 .10 .05 .00 −.05

FIN-V COM-R

COM-V

.10 .05

.016

.00

.012

−.05

.008

−.10

.01

.004

.00 1986 1988 1990 1992 1994 1996 1998 2000

.000 1986 1988 1990 1992 1994 1996 1998 2000

POL-R

COM-R

POL-V

.15

COM-V

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 325

Figure 4.113. Risk ratings, risk returns and volatilities for the Netherlands RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R 92

FIN-V

FIN-R 100 90

88

80

84 80

40

76

30 20 10 0

84

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-V

POL-R 100

70

500 400 300 200 100 0

60

84

86

88

90 92 94 96 98 FIN-R FIN-V

00

COM-V

COM-R 94 92 90 88 86 84 82

96 80 60

92

25

88

20

84

15

80

40

10

20

5

0

0

84

86

88

90 92 94 96 98 POL-R POL-V

00

84

86

88

90 92 94 96 98 COM-R COM-V

00

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

.0025 .0020 .0015 .0010 .0005 .0000

84

ECO-R .04 .02 .00 −.02 −.04 −.06

86

88

90 92 94 96 98 ECO-R ECO-V

00

POL-R .03 .02 .01 .00 −.01 −.02 −.03

.0008 .0006 .0004 .0002 84

86

88

90 92 94 96 98 POL-R POL-V

.06 .05 .04 .03 .02 .01 .00

84

FIN-R .2 .1 .0 −.1 −.2 −.3

86

88

90

92

FIN-R

POL-V

.0000

FIN-V

00

94

96

98

00

FIN-V

COM-V

.0025 .0020 .0015 .0010 .0005 .0000

84

COM-R .04 .02 .00 −.02 −.04 −.06

86

88

90 92 94 96 98 COM-R COM-V

00

326

S. Hoti and M. McAleer

Figure 4.114. Risk ratings, risk returns and volatilities for Norway RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

150 100

100 95 90 85 80 75

50 0

84

86

88

90

92

94

ECO-R

96

98

00

FIN-R

92 50 40 30 20 10 0

88 84

84

86

88

90

92

94

FIN-R POL-R

80 60

96 92 88 84 80 76

40

96

98

00

FIN-V

COM-V

COM-R

84

86

88

90

92

94

POL-R

96

98

90 88

30

86

20

0

00

94 92

40

84 82

10

20

100 96

ECO-V

POL-V

0

FIN-V

84

86

88

90

92

94

COM-R

POL-V

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

FIN-V

.10

FIN-R

.04

.05 .00

.010 .008

−.05

.006 .004 .002 .000

84

86

88

90

92

94

ECO-R

96

98

00

.00 .005 .004 .003 .002 .001 .000

−.04 −.08

84

86

88

90

92

94

FIN-R

ECO-V

POL-V

POL-R

96

98

00

FIN-V

COM-V

.10

COM-R

.05 .00 −.05 .010 .008 .006 .004 .002 .000

−.10

86

88

90

92

POL-R

94

96 POL-V

98

00

.04 .02

.0012

.00 −.02

.0008

−.04

.0004

84

.08

.0000

84

86

88

90

92

COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 327

Figure 4.115. Risk ratings, risk returns and volatilities for Portugal RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

240 200 160 120 80 40 0

90 85 80 75 70 65 60

90 80 70

400

60

300 200 100

84

86

88

90

92

94

ECO-R

96

98

0

00

84

86

88

ECO-V

90

92

94

FIN-R POL-R

POL-V

200 150 100

95 90 85 80 75 70 65

96

98

00

FIN-V COM-R

COM-V

120

90 85 80 75 70 65

80 40

50 0

FIN-R

FIN-V

84

86

88

90

92

94

POL-R

96

98

0

00

84

86

88

POL-V

90

92

94

COM-R

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

FIN-R

FIN-V

.08 .04

.0

.00 .006 .005 .004 .003 .002 .001 .000

84

86

88

90

92

94

ECO-R

96

98

−.04

.05

−.08

.04

−.2

.03 .02

−.3

.01 .00

00

84

86

88

90

92

94

FIN-R POL-R

96

98

COM-R

COM-V

.08

00

FIN-V

.00 −.04 −.08

84

86

88

90

92

POL-R

94

96 POL-V

98

00

.08 .04

.04

.006 .005 .004 .003 .002 .001 .000

−.1

ECO-V

POL-V

.1

.00 .005 .004 .003 .002 .001 .000

−.04 −.08

84

86

88

90

92

COM-R

94

96

98

COM-V

00

328

S. Hoti and M. McAleer

Figure 4.116. Risk ratings, risk returns and volatilities for Spain RISK RATINGS ANDASSOCIATED VOLATILITIES ECO-V

ECO-R

FIN-V

85

FIN-R

80 75 100 80 60 40 20 0

70 65

100 50 84

86

88

90

92

94

ECO-R

96

98

0

00

84

86

88

90

92

94

FIN-R

ECO-V

POL-V

120 100 80 60 40 20 0

150

88 84 80 76 72 68

POL-R

96

98

COM-V

85 80 75 70 65

80

60

60

00

FIN-V COM-R

84 80 76 72 68 64

40 20 84

86

88

90

92

94

POL-R

96

98

0

00

84

86

88

90

92

94

COM-R

POL-V

96

98

00

COM-V

RISKRETURNS AND ASSOCIATED VOLATILITIES ECO-V

ECO-R

FIN-V

.15

FIN-R

.10

.08 .04

.05

.00

.020

.00

−.04

.015

−.05 −.10

.010 .005 .000

84

86

88

90

92

94

ECO-R

96

98

00

.006 .005 .004 .003 .002 .001 .000

−.08

84

86

88

ECO-V

90

92

94

FIN-R

POL-V

POL-R

96

98

COM-V

.12

00

FIN-V COM-R

.00

.006

−.04

.004

−.08

.002 .000

.02

.04

.008

.0025

.00

.0020

−.02

.0015

−.04

.0010 .0005

84

86

88

90

92

POL-R

94

96 POL-V

98

00

.06 .04

.08

.0000

84

86

88

90

92

COM-R

94

96

98

COM-V

00

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 329

Figure 4.117. Risk ratings, risk returns and volatilities for Sweden RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

200 150 100

95 90 85 80 75 70 65

50 0

84

86

88

90

92

94

ECO-R

96

98

00

80 600 500 400 300 200 100 0

80 60

96 92 88 84 80 76

92

88

94

POL-R

96

98

90

92

94

98

00

COM-R

92

80 76

30

0

00

96 FIN-V

84

10 90

86

40

20 88

84

88

20

86

60

COM-V

40

84

70

FIN-R POL-R

100 90

ECO-V

POL-V

0

FIN-R

FIN-V

84

86

88

90

92

94

COM-R

POL-V

96

98

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.015

.15 .10 .05 .00 −.05 −.10 −.15

.010 .005 .000

84

86

88

90

92

94

ECO-R

96

98

84

86

90

92

POL-R

84

86

88

94

96 POL-V

90

92

94

FIN-R POL-R

88

.05 .04 .03 .02 .01 .00

ECO-V

POL-V

.0024 .0020 .0016 .0012 .0008 .0004 .0000

00

FIN-R

FIN-V

98

00

.06 .04 .02 .00 −.02 −.04 −.06

96

98

84

86

00

FIN-V COM-R

COM-V

.0030 .0025 .0020 .0015 .0010 .0005 .0000

88

90

92

COM-R

.2 .1 .0 −.1 −.2 −.3

94

96

98

COM-V

00

.06 .04 .02 .00 −.02 −.04 −.06

330

S. Hoti and M. McAleer

Figure 4.118. Risk ratings, risk returns and volatilities for Switzerland RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

120 80

95 90 85 80 75 70

40 0

84

86

88

90

92

ECO-R

94

96

98

00

FIN-R

FIN-V

240 200 160 120 80 40 0

100 95 90 85 80

84

86

88

ECO-V

90

92

FIN-R POL-R

POL-V

94

96

98

COM-R

COM-V

100

00

FIN-V

96 92 50

88

40

84

30

80

20 10 0

84

86

88

90

92

POL-R

94

96

98

00

25 20 15 10 5 0

84

86

88

POL-V

90

92

COM-R

94

96

98

96 94 92 90 88 86 84

00

COM-V

RISK RETURNS AND ASSOCIATED VOLATILITIES ECO-R

ECO-V

.016 .012

.15 .10 .05 .00 −.05 −.10 −.15

.05

.016

.00

.012

−.05 −.10

.004

.004 84

86

88

90

92

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Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 331

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Figure 4.120. Risk ratings, risk returns and volatilities for the UK RISK RATINGS AND ASSOCIATED VOLATILITIES ECO-V

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Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 333

Appendix 4.1. ICRG classiﬁcation of countries by starting date and geographic region Country Bangladesh India Pakistan Sri Lanka Australia Brunei China Hong Kong Indonesia Japan Malaysia Mongolia New Zealand North Korea Papua New Guinea Philippines Singapore South Korea Taiwan Thailand Vietnam Albania Bulgaria Czech Republic Hungary Poland Romania Russia Slovak Republic Yugoslavia Algeria Bahrain Egypt Iran Iraq Israel Jordan Kuwait Lebanon Libya Morocco Oman Qatar

ICRG Starting Date January 1984 January 1984 January 1984 January 1984 January 1984 November 1985 December 1984 January 1984 January 1984 January 1984 January 1984 April 1986 January 1984 October 1985 May 1984 January 1984 January 1984 March 1985 January 1984 January 1984 October 1985 October 1985 December 1984 January 1993 August 1984 December 1984 August 1984 April 1992 January 1984 January 1993 January 1984 September 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 July 1984 August 1984

Geographic Region Central and South Asia Central and South Asia Central and South Asia Central and South Asia East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Asia and the Paciﬁc East Europe East Europe East Europe East Europe East Europe East Europe East Europe East Europe East Europe Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa (continued)

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Appendix 4.1. Continued Country Saudi Arabia Syria Tunisia United Arab Emirates Yemen Bahamas Canada Costa Rica Cuba Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Trinidad and Tobago USA Argentina Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR of Congo Ethiopia Gabon Ghana Guinea Kenya Liberia

ICRG Starting Date January 1984 January 1984 January 1984 January 1984 September 1984 December 1985 January 1984 January 1984 October 1985 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 October 1985 October 1984 May 1985 January 1984 April 1985 September 1986 January 1984 November 1984 January 1984 January 1984 October 1985 January 1984 January 1984

Geographic Region Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa Middle East and North Africa North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America North and Central America South America South America South America South America South America South America South America South America South America South America Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa (continued)

Assessment of Risk Ratings and Risk Returns for 120 Representative Countries 335

Appendix 4.1. Continued Country Malawi Mali Mozambique Nigeria Senegal Sierra Leone South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal Spain Sweden Switzerland Turkey United Kingdom

ICRG Starting Date January 1984 August 1984 October 1985 January 1984 January 1984 January 1985 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 December 1984 January 1984 January 1984 January 1984 January 1985 January 1984 January 1984 January 1984 January 1984 December 1984 April 1986 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984 January 1984

Source: The PRS Group, Inc., 2003

Geographic Region Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa Sub-Saharan Africa West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe West Europe

CHAPTER 5

Conditional Volatility Models for Risk Ratings and Risk Returns Abstract This chapter reviews the most recent theoretical results on univariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models of conditional volatility and discusses the constant correlation asymmetric VARMA – GARCH model of Hoti, Chan and McAleer (Australasian Meeting of the Econometric Society, Brisbane, Australia, July 2002). The underlying structure of the VARMA– AGARCH model is examined, including convenient sufﬁcient conditions for the existence of moments for empirical analysis. Alternative sufﬁcient conditions for the consistency and asymptotic normality of the quasi-maximum likelihood estimator are given under non-normality of the standardized shocks. These conditions permit an empirical evaluation of the usefulness of the models for analysing country risk ratings and risk returns, and their associated volatility. Keywords: multivariate conditional volatility, asymmetric effects, spillovers, conditional correlations, structural properties, regularity conditions, asymptotic theory JEL classiﬁcations: C12, C13, C22, C51 5.1. Introduction Time series data relating to risk ratings and risk returns contain both conditional mean and conditional variance (or volatility) components, both of which may vary over time. Volatility is used in risk analysis for examining portfolio selection, asset management, valuation of warrants and options, modelling the premium in forward and futures prices, evaluation of risk spillovers across markets, designing optimal hedging

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strategies for options and futures markets and measuring the announcement effects in event studies, among others. Moreover, derivative assets are used to hedge against commodity price risk and to hedge against issued bonds. As such, optimal hedging strategies and an evaluation of the risks underlying risk ratings require knowledge of the volatility of the underlying stochastic process. As volatility is generally unknown, it must be estimated. Such estimated and predicted volatilities are fundamental to risk management in ﬁnancial portfolio models that describe the trade-off between risk and returns. Estimating and testing the volatility associated with risk ratings would seem to be a ﬁrst step in establishing a market for pricing risk ratings as a primary or derivative asset. Conditional volatility has been used to evaluate risk, asymmetric shocks and leverage effects in economics and ﬁnance (see McAleer (2005) for a comparison of univariate and multivariate conditional volatility and stochastic volatility models). Volatility that is present in country risk ratings will naturally reﬂect risk considerations inherent in such ratings. For this reason, the rate of change in risk ratings, i.e. their underlying returns, merits the same attention as has been bestowed on ﬁnancial returns. If risk returns vary over time, they can be modelled using time series methods. Engle (1982) developed the autoregressive conditional heteroscedasticity, or ARCH(p), model to capture time-varying volatility, and this was subsequently generalized to the generalized autoregressive conditional heteroscedasticity or GARCH(p; q), model by Bollerslev (1986). These time-varying conditional volatility models have several attractive features, such as the ability to capture persistence of volatility, volatility clusters, thick-tailed distributions and even an inﬁnite unconditional variance of the shocks. In many cases in practice, positive and negative shocks can have asymmetric effects, with negative shocks having a greater effect on volatility than positive shocks. Glosten et al. (1992) extended the univariate GARCHðp; qÞ model to the univariate GJRðp; qÞ model by introducing threshold effects (or asymmetry) into the conditional volatility process. An extension of the multivariate GARCHðp; qÞ model to accommodate the multivariate asymmetric effects of unconditional shocks was developed in Hoti et al. (2002). 5.2. Univariate conditional volatility models Numerous theoretical and empirical developments in modelling the various moments of time series processes have been made in the economics, ﬁnance and ﬁnancial econometrics literature. Central to the analysis of time-varying

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risk, or volatility, is the modelling of unconditional and conditional second moments. The two most common methods of analysing volatility are procedures for modelling conditional volatility and stochastic volatility. At present, models of conditional volatility are more well developed and more computationally feasible than models of stochastic volatility, especially for multivariate processes, and hence are more widely used in practice (see McAleer (2004)). As one of the primary purposes of this chapter is to use a multivariate approach to analyse risk using risk ratings and risk returns data, the theoretical analysis and empirical models will be based on the conditional volatility approach. Engle’s (1982) ARCH model and Bollerslev’s (1986) GARCH model have been widely used to capture time-varying symmetric volatility for ﬁnancial and economic time series data. This section discusses some of the most recent theoretical results on univariate GARCH models. Comprehensive surveys of theoretical and empirical developments associated with univariate GARCH models are given in Bollerslev et al. (1992), Bera and Higgins (1993) and Bollerslev et al. (1994). Several important structural and asymptotic results underlying a range of estimation methods have recently been established for a wide variety of GARCH models. Li et al. (2002) survey recent theoretical results regarding the structure and asymptotic theory for GARCH models, all of which provide a solid theoretical and statistical foundation for applying the various models in practice. Theoretical results underlying the structure and estimation of GARCH models include convenient sufﬁcient conditions for the existence of moments (such as the second moment to capture conditional volatility or risk, third moment to capture conditional skewness and fourth moment to capture conditional kurtosis or the existence of extreme observations and outliers) and for the quasi maximum likelihood estimators (QMLE) to be consistent and asymptotically normal. Theoretical results regarding the structure have been established for some asymmetric models. The asymptotic theory for the GJRðp; qÞ model was developed by McAleer et al. (2002). In this chapter, the statistical properties of the asymmetric VARMA– GARCH, or VARMA– AGARCH, model are presented to enable empirical veriﬁcation in practice. Let u ¼ ðf; v; a; bÞ0 be the vector of parameters to be estimated, with f ¼ ðf1 ; f2 ; …; fr Þ0 ; a ¼ ða1 ; …; ap Þ0 ; b ¼ ðb1 ; …; bq Þ0 ; r þ p þ q ¼ k: Deﬁne yt as a GARCHðp; qÞ process, as follows: yt ¼ f ðxt ; fÞ þ 1t ; pﬃﬃﬃ 1t ¼ ht ht ;

t ¼ 1; …; n

ht , iidð0; 1Þ

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ht ¼ v þ

p X i¼1

ai 12t2i þ

q X i¼1

bi ht2i

where the standardized shocks, hit ; are independently and identically distributed, xt ¼ ðyt21 ; yt22 ; …; 1t21 ; 1t22 ; …; zt Þ0 and zt is a 1 £ g vector of exogenous variables. Moreover, it is assumed that ai . 0 for all i ¼ 1; …; p and bi . 0 for all i ¼ 1; …; q to ensure the positivity of the conditional variance, ht : When q ¼ 0; GARCH( p; q) collapses to ARCH( p). Deﬁne the likelihood function as ! n 1 X 12t lðuÞ ¼ 2 log ht þ : 2n t¼1 ht For purposes of estimation, the maximum likelihood estimator (MLE) of the parameters of the GARCH(p; q) model depends on the normality of the standardized residuals, ht ; otherwise, the estimator is deﬁned as the QMLE. Weiss (1986) and Pantula (1989) showed that Eð14t Þ , 1; namely the existence of the fourth moment, is sufﬁcient for consistency and asymptotic normality for the QMLE in the case of q ¼ 0 and f ðxt ; fÞ ¼ c: This result was extended by Ling and McAleer (2003), who showed that the QMLE is consistent and asymptotically normal if Eð12t Þ , 1; namely the existence of the second moment. For q . 0 and f ðxt ; fÞ ¼ f0 xt ; Bollerslev (1986) showed that the necessary and sufﬁcient condition for GARCH( p; q) to have ﬁnite second moments is p X i¼1

ai þ

q X i¼1

bi , 1:

Under the assumption that b1 . 0; Nelson (1990) derived the necessary and sufﬁcient log– moment condition for GARCH(1,1). This condition is not easy to apply in practice as it involves the expectation of a function of an unknown random variable and unknown parameters. However, it is attractive because the condition allows the long-run persistence, namely a1 þ b1 ; to be greater than one. Ling and McAleer (2002b) established the sufﬁcient condition for the stationary solution of a family of GARCH(1,1) models investigated by He and Tera¨svirta (1999a) with f ðxt ; fÞ ¼ 0: Ling and McAleer (2002b) also showed that the moment condition in He and Tera¨svirta (1999a) is necessary but not sufﬁcient, and provided the sufﬁcient condition. He and Tera¨svirta (1999b) investigated the fourth moment structure of

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the GARCH(p; q) process with f ðxt ; fÞ ¼ 0: Ling (1999) obtained a sufﬁcient condition for the existence of the 2mth moment for this model. The sufﬁcient condition derived in Ling (1999) is also necessary for the existence of the 2mth moment for GARCH( p; q) when f ðxt ; fÞ follows an autoregressive moving average (ARMA) process (see Ling and McAleer (2002a, 2003)). Furthermore, Ling and McAleer (2002a) also derived the necessary and sufﬁcient moment conditions of the asymmetric power GARCH( p; q) model of Ding et al. (1993). Estimation of the parameters of the ARMA– GARCH( p; q) model is typically by MLE, or by QMLE when ht is not normal. Ling and Li (1997) showed that the local QMLE for GARCH( p; q) is consistent and asymptotically normal if Eð14t Þ , 1: For the global QMLE, Ling and McAleer (2003) showed that Eð12t Þ , 1 is sufﬁcient for consistency, and Eð16t Þ , 1 is sufﬁcient for asymptotic normality. Elie and Jeantheau (1995) and Jeantheau (1998) showed that the log –moment condition is sufﬁcient for consistency of QMLE for GARCH( p; q), while Boussama (2000) proved that the same condition is also sufﬁcient for asymptotic normality. McAleer et al. (2002) showed that the moment conditions in Ling and McAleer (2002a, 2003), and the conditions for consistency and asymptotic normality in Elie and Jeantheau (1995), Jeantheau (1998), Boussama (2000), Ling and McAleer (2003), also hold for the GJR model. Hoti et al. (2002) established the VARMA– AGARCH model, and showed that the moment conditions and consistency results in Ling and McAleer (2003), the consistency results in Elie and Jeantheau (1995) and Jeantheau (1998) and the asymptotic normality result in Ling and McAleer (2003), also applied to their model. 5.3. An asymmetric VARMA –GARCH model Multivariate extensions of several GARCH models are available in the literature (see, for example, Engle et al. (1984), Bollerslev et al. (1988), Engle and Rodrigues (1989), Bollerslev (1990), Ling and Deng (1993), Engle and Kroner (1995), Wong and Li (1997) and Ling and McAleer (2003), among others). The primary purpose in each of these papers has typically been to examine the structure of the model rather than to derive the asymptotic properties of the estimators, with exceptions being Ling and Deng (1993), Jeantheau (1998), Comte and Lieberman (2003) and Ling and McAleer (2003). Even when statistical properties have been considered, they are either assumed to hold or sufﬁcient regularity conditions are imposed to ensure consistency and asymptotic normality. Three exceptions to the rule are the VARMA –GARCH and VARMA– AGARCH models of Hoti et al. (2002) and Ling and McAleer (2003),

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for which the structural properties have been developed, the analytical forms of the regularity conditions derived and the asymptotic theory for the QMLE established under multivariate fourth moments and the BEKK model of Engle and Kroner (1995), for which Comte and Lieberman (2003) assumed the existence of moments, showed consistency of the QMLE using the conditions in Jeantheau (1998), and established asymptotic normality of the QMLE under eighth moments. In this section, the static (or constant) conditional correlation VARMA – AGARCH model is presented. This model includes the constant conditional correlation (CCC) model of Bollerslev (1990) and the VARMA– GARCH model of Ling and McAleer (2003) as special cases. Consistency is obtained under the weak multivariate log – moment condition and the computationally more straightforward second moment condition, and asymptotic normality of the QMLE is obtained under the fourth moment condition. Bollerslev (1990) presented an m-dimensional multivariate conditional covariance model, as follows: Yt ¼ EðYt lFt21 Þ þ 1t ;

t ¼ 1; …; n

1t ¼ Dt ht

ð5:1Þ

Varð1t lFt21 Þ ¼ Dt GDt 1=2

where Ft is the information set available at time t; Dt ¼ diagðhit Þ; i ¼ 1; …; m; is a diagonal matrix of the conditional variances and G ¼ {rij } is the matrix of static (or constant) conditional correlations, in which rij ¼ rji for i; j ¼ 1; …; m: The main feature of this model is that the conditional correlation is constant over time, where i – j; i; j ¼ 1; …; m; and 1it is the ith element of 1t : Bollerslev’s (1990) CCC model assumed that

hit ¼ vi þ

r X l¼1

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s X l¼1

bil hit2l ;

i ¼ 1; …; m

ð5:2Þ

in which there is no interdependence (an hence no spillovers) between hit and ð1jt2k ; hjt2l Þ for i – j; i; j ¼ 1; …; m; k ¼ 1; …; r; and l ¼ 1; …; s: The CCC model explicitly assumes independence of volatilities across different risk ratings and risk returns, and across countries, so that the multivariate effects are determined solely through the CCC matrix, G:

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An extension of Equation (5.2) to accommodate asymmetries with respect to 1it is given by hit ¼ vi þ

r X l¼1

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gil Iðhit2l Þ12it2l þ

s X l¼1

bil hit2l ;

i ¼ 1; …; m ð5:3Þ pﬃﬃﬃ in which 1it ¼ hit hit for all i and t; and Iðhit Þ is an indicator variable to distinguish between positive and negative shocks, such that ( 1; 1it # 0 Iðhit Þ ¼ 0; 1it . 0: Let ht ¼ ðh1t ; …; hmt Þ0 be a sequence of independently and identically distributed random vectors, with zero mean and covariance G, so that 1t ¼ Dt ht ; in which Dt depends only on Ht ¼ ðh1t ; …; hmt Þ0 : The multivariate effects are still determined through the CCC matrix, G: As an extension of Equation (5.3) to incorporate spillover effects across risk ratings, risk returns countries, it is necessary to model hit on the basis of past information from 1it ; 1jt ; hit and hjt for i – j; i; j ¼ 1; …; m: Thus, the VARMA –AGARCH of Hoti et al. (2002) is deﬁned as follows:

FðLÞðYt 2 mÞ ¼ CðLÞ1t ; 1t ¼ Dt ht Ht ¼ W þ

r X l¼1

Al 1~t2l þ

r X l¼1

Cl Iðht2l Þ1~t2l þ

ð5:4Þ s X l¼1

Bl Ht2l

ð5:5Þ

1=2

where Dt ¼ diagðhit Þ; Al ; Cl and Bl are m £ m matrices with typical elements aij ; gij and bij ; respectively, for i; j ¼ 1; …; m; Iðht Þ ¼ diagðIðhit ÞÞ is an m £ m matrix, FðLÞ and CðLÞ are polynomials in L; Ik is the k £ k identity matrix and 1~t ¼ ð121t ; …; 12mt Þ0 : The parameter vector is given by l ¼ ðw0 ; d0 ; r0 Þ0 ; where

w ¼ vecðm; F1 ; …; Fp ; C1 ; …; Cq Þ d ¼ vecðW; A1 ; …; Ar ; C1 ; …; Cr ; B1 ; …; Bs Þ r ¼ ðr21 ; …; rm1 ; r32 ; …; rm2 ; …; rm;m21 Þ0 : The univariate constant-mean GJR model is obtained from Equations (5.4) and (5.5) either by setting m ¼ 1 and FðLÞ ¼ CðLÞ ¼ 1; or by specifying Al ; Cl and Bl as diagonal matrices. Bollerslev’s (1990) multivariate model (5.2) is obtained from Equations (5.4) and (5.5) by setting Al ¼ diagðail Þ; Bl ¼ diagðbil Þ and Cl ¼ 0 for l ¼ 1; …; r; while

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Ling and McAleer’s (2003) VARMA– GARCH model is obtained from Equations (5.4) and (5.5) by setting Cl ¼ 0 for l ¼ 1; …; r: The QMLE of the parameters in models (5.4) and (5.5) are obtained by maximizing the following conditional log – likelihood function Ln ðlÞ ¼

n 1X l ðlÞ n t¼1 t

1 1 lt ðlÞ ¼ 2 loglDt GDt l 2 10t ðDt GDt Þ21 1t 2 2

ð5:6Þ

where Ln ðlÞ takes the form of the Gaussian log –likelihood, G is deﬁned in 1=2 Equation (5.1) and Dt ¼ diagðhit Þ: Since it is not assumed that h0t is normal, the estimators from Equation (5.6) are the QMLE. Jeantheau (1998) proved consistency of the QMLE by assuming a multivariate log – moment condition, which is weaker than the second moment condition. Hoti et al. (2002) proved consistency of the QMLE under the assumption of second moments, and asymptotic normality under the assumption of fourth moments. Boussama (2000) showed that the univariate version of the log – moment condition is sufﬁcient for asymptotic normality of the QMLE for the GARCH(p; q) model. For the univariate GJR(1,1) process when m ¼ r ¼ s ¼ 1; McAleer et al. (2002) showed that the log– moment condition is given by Eðlog½ða þ gIðht ÞÞh2t þ b Þ , 0:

ð5:7Þ

A special case of Equation (5.7) when g ¼ 0 is the well-known log– moment condition for GARCH(1,1), namely Eðlogðah2t þ bÞÞ , 0

ð5:8Þ

(see Nelson (1990) and Lee and Hansen (1994)). The second moment condition for the GJR(1,1) model is given as

a þ b þ 12 g , 1

ð5:9Þ

(see Ling and McAleer (2002b)). A special case of Equation (5.9) when g ¼ 0 is the well-known second moment condition for GARCH(1,1), namely

a þ b , 1:

ð5:10Þ

The conditions in Equations (5.7) –(5.10) for the univariate case, m ¼ 1; are straightforward to check, and hence provide useful diagnostic information regarding the regularity conditions for sensible empirical analysis.

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It is clear from Equations (5.7) and (5.8) that the log – moment conditions involve the expectation of a function of a random variable and unknown parameters. Although the log – moment conditions in Equations (5.7) and (5.8) are sufﬁcient for the QMLE of the GJR(1,1) and GARCH(1,1) models to be consistent and asymptotically normal, the stronger second moment conditions given in Equations (5.9) and (5.10), respectively, are more straightforward to check in practice as they do not involve the mean of a logarithmic function of a random variable. Moreover, the second moment condition can easily be used to verify consistency and asymptotic normality in the event that the log – moment condition cannot be computed when ða þ gIðht ÞÞh2t þ b , 0 in Equation (5.7) or ah2t þ b , 0 in Equation (5.10) for any t ¼ 1; …; n: For the GARCH(1,1) model, the ARCH (or a) effect indicates the shortrun persistence of shocks to risk ratings or risk returns, while the GARCH (or b) effect indicates the contribution of shocks to long-run persistence (namely, a þ b), as in Equation (5.8). The asymmetric effect, g; in the GJR(1,1) model measures the contribution of shocks to both short-run persistence, a þ g=2; and to long-run persistence, a þ b þ g=2; as in Equation (5.9). Sufﬁcient conditions for hit . 0 in GARCH(1,1) are vi . 0; ai $ 0 and bi $ 0 for i ¼ 1; …; m; while GJR(1,1) requires vi . 0; ai þ gi $ 0 and bi $ 0 for hit . 0 for i ¼ 1; …; m: However, in the ﬁnancial risk literature, negative shocks increase risk so that gi is generally expected to be positive. 5.4. Conclusion This chapter reviewed the most recent theoretical results on univariate GARCH models of conditional volatility. The constant correlation asymmetric VARMA– GARCH model of Hoti et al. (2002) was discussed. The underlying structure of the VARMA – AGARCH model was examined, including convenient sufﬁcient regularity conditions for sensible empirical analysis. Alternative sufﬁcient conditions for the consistency and asymptotic normality of the QMLE were given under non-normality of the standardized shocks. These conditions permit an empirical evaluation of the usefulness of the models for analysing country risk ratings and risk returns, and their associated volatility. References Bera, A.K. and M.L. Higgins (1993), “ARCH models: properties, estimation and testing”, Journal of Economic Surveys, Vol. 7, pp. 305 – 366, Reprinted in L. Oxley,

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D.A.R. George, C.J. Roberts and S. Sayer (eds.) (1995), Surveys in Econometrics, Oxford: Blackwell, pp. 215– 272. Bollerslev, T. (1986), “Generalised autoregressive conditional heteroscedasticity”, Journal of Econometrics, Vol. 31, pp. 307– 327. Bollerslev, T. (1990), “Modelling the coherence in short-run nominal exchange rate: a multivariate generalized ARCH approach”, Review of Economics and Statistics, Vol. 72, pp. 498– 505. Bollerslev, T., R.F. Engle and J.M. Wooldridge (1988), “A capital asset pricing model with time varying covariance”, Journal of Political Economy, Vol. 96, pp. 116– 131. Bollerslev, T., R.Y. Chou and K.F. Kroner (1992), “ARCH modelling in ﬁnance: a review of the theory and empirical evidence”, Journal of Econometrics, Vol. 52, pp. 5 – 59. Bollerslev, T., R.F. Engle and D.B. Nelson (1994), “ARCH models”, pp. 2961– 3038 in: R.F. Engle and D.L. McFadden, editors, Handbook of Econometrics, Vol. 4, Amsterdam: North-Holland. Boussama, F. (2000), “Asymptotic normality for the quasi-maximum likelihood estimator of a GARCH model”, Comptes Rendus de l’Academie des Sciences, Serie I, Vol. 331, pp. 81 – 84, in French. Comte, F. and O. Lieberman (2003), “Asymptotic theory for multivariate GARCH processes”, Journal of Multivariate Analysis, Vol. 84, pp. 61 –84. Ding, Z., C.W.J. Granger and R.F. Engle (1993), “A long memory property of stock market returns and a new model”, Journal of Empirical Finance, Vol. 21, pp. 83 – 106. Elie, L. and T. Jeantheau (1995), “Consistency in heteroskedastic models”, Comptes Rendus de L’Academie des Sciences, Serie I, Vol. 320, pp. 1255– 1258, in French. Engle, R.F. (1982), “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inﬂation”, Econometrica, Vol. 50, pp. 987– 1007. Engle, R.F. and K.F. Kroner (1995), “Multivariate simultaneous generalized ARCH”, Econometric Theory, Vol. 11, pp. 122– 150. Engle, R.F. and A.P. Rodrigues (1989), “Tests of international CAPM with time varying covariances”, Journal of Applied Econometrics, Vol. 4, pp. 119– 128. Engle, R.F., C.W.J. Granger and D.F. Kraft (1984), “Combining competing forecasts of inﬂation using a bivariate ARCH model”, Journal of Economic Dynamics and Control, Vol. 8, pp. 151– 165. Glosten, L., R. Jagannathan and D. Runkle (1992), “On the relation between the expected value and volatility of nominal excess return on stocks”, Journal of Finance, Vol. 46, pp. 1779– 1801. He, C. and T. Tera¨svirta (1999a), “Properties of moments of a family of GARCH processes”, Journal of Econometrics, Vol. 92, pp. 173–192. He, C. and T. Tera¨svirta (1999b), “Fourth moment structure of the GARCH( p; q), process”, Econometric Theory, Vol. 15, pp. 824– 846. Hoti, S., F. Chan and M. McAleer (2002), “Structure and asymptotic theory for multivariate asymmetric volatility: empirical evidence for country risk ratings”, Invited Paper Presented at the Australasian Meeting of the Econometric Society, Brisbane, Australia, July 2002. Jeantheau, T. (1998), “Strong consistency of estimators for multivariate ARCH models”, Econometric Theory, Vol. 14, pp. 70 – 86. Lee, S.W. and B.E. Hansen (1994), “Asymptotic theory for the GARCH(1,1) quasimaximum likelihood estimator”, Econometric Theory, Vol. 10, pp. 29 – 52. Li, W.K., S. Ling and M. McAleer (2002), “Recent theoretical results for time series models with GARCH errors”, Journal of Economic Surveys, Vol. 16, pp. 245– 269, reprinted in

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M. McAleer and L. Oxley, editors, Contributions to Financial Econometrics: Theoretical and Practical Issues, Oxford: Blackwell, pp. 9 – 33. Ling, S. (1999), “On the probabilistic properties of a double threshold ARMA conditional heteroskedasticity model”, Journal of Applied Probability, Vol. 36, pp. 1 –18. Ling, S. and W.C. Deng (1993), “Parametric estimate of multivariate autoregressive models with conditional heterocovariance matrix errors”, Acta Mathematicae Applicatae Sinica, Vol. 16, pp. 517– 533, in Chinese. Ling, S. and M. McAleer (2002a), “Necessary and sufﬁcient moment conditions for the GARCH(r; s) and asymmetric power GARCH(r; s) models”, Econometric Theory, Vol. 18, pp. 722– 729. Ling, S. and M. McAleer (2002b), “Stationarity and the existence of moments of a family of GARCH processes”, Journal of Econometrics, Vol. 106, pp. 109– 117. Ling, S. and M. McAleer (2003), “Asymptotic theory for a vector ARMA– GARCH model”, Econometric Theory, Vol. 19, pp. 278– 308. Ling, S. and W.K. Li (1997), “On fractionally integrated autoregressive moving-average models with conditional heteroskedasticity”, Journal of the American Stastical Association, Vol. 92, pp. 1184– 1194. McAleer, M. (2005), “Automated Inference and Learning in Modeling Financial Volatility”, Econometric Theory, Vol. 21, pp. 232– 261. McAleer, M., F. Chan and D. Marinova (2002), “An econometric analysis of asymmetric volatility: theory and application to patents”, Invited Paper Presented at the Australasian meeting of the Econometric Society, Brisbane, Australia, July 2002, to appear in Journal of Econometrics. Nelson, D.B. (1990), “Stationarity and persistence in the GARCH(1,1) model”, Econometric Theory, Vol. 6, pp. 318– 334. Pantula, S.G. (1989), “Estimation of autoregressive models with ARCH errors”, Sankhya B, Vol. 50, pp. 119– 138. Weiss, A.A. (1986), “Asymptotic theory for ARCH models: estimation and testing”, Econometric Theory, Vol. 2, pp. 107– 131. Wong, H. and W.K. Li (1997), “On a multivariate conditional heteroscedasticity model”, Biometrika, Vol. 4, pp. 111– 123.

CHAPTER 6

Univariate and Multivariate Estimates of Symmetric and Asymmetric Conditional Volatilities and Conditional Correlations for Risk Returns Abstract This chapter provides a comparison of monthly ICRG country risk ratings and risk returns. As risk ratings can be treated as indexes, their rates of change, or risk returns, are analysed in the same manner as ﬁnancial returns. The empirical results provide a comparative assessment of the conditional means and volatilities associated with risk returns across alternative risk ratings and countries over time. Based on the monthly standardized residuals of the univariate AR(1)– GARCH(1,1) models, the corresponding static conditional correlations are calculated for the economic, ﬁnancial, political and composite risk return shocks for the 120 countries. Keywords: risk ratings, risk returns, shocks, conditional volatility, conditional correlations, regularity conditions, asymmetry, GARCH, GJR, CCC JEL classiﬁcations: C22, C51, E44

6.1. Introduction and recommendations for foreign investors Monthly data can capture the time-varying volatility that is inherent in the underlying series. As risk ratings can be treated as indexes, their rates of change, or risk returns, are analysed in the same manner as ﬁnancial returns. This chapter uses monthly ICRG country risk returns to estimate and test univariate and multivariate models of conditional volatility. The univariate and multivariate empirical results enable a validation of the regularity

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conditions underlying the model, highlight the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating, and evaluate the practical usefulness of the ICRG risk ratings. Univariate GARCH (Bollerslev, 1986) and GJR (Glosten et al., 1992) models are estimated for four risk returns, namely economic, ﬁnancial, political and composite, for each of the 120 representative countries. However, the main purpose of this chapter is to estimate the static (or constant) conditional correlations of shocks to risk returns across countries. This gives an indication of the relationship between shocks to the economic, ﬁnancial, political and composite risk returns, as well as foreign investment decisions regarding the economic, ﬁnancial, political and composite risk ratings across the countries in the eight geographical regions. An understanding of the relationship between country risk ratings and risk returns, and the conditional correlations of shocks between pairs of risk returns, are essential for optimal decision making about foreign investments. Three important values of conditional correlations between pairs of shocks to risk returns are as follows: (1) Conditional correlation ¼ 1: As the shocks to the risk returns for the two countries move in the same direction, foreign investors should decide to invest on the basis of the risk ratings of both countries and the country with the higher risk returns. Thus, if both countries have the same risk rating, the foreign investor should choose the country with the higher risk returns. (2) Conditional correlation ¼ 0: As the shocks to the risk returns for the two countries are uncorrelated, foreign investors should decide to invest on the basis of the risk ratings and risk returns of both countries. Thus, if both countries have the same risk returns, the foreign investor should choose the country with the higher risk rating; if both countries have the same risk rating, the foreign investor should choose the country with the higher risk returns. (3) Conditional correlation ¼ 21: As the shocks to the risk returns for the two countries move in opposite directions, foreign investors should hedge and invest in both countries according to their respective risk ratings. All the estimates in this chapter are obtained using EViews 4. The Berndt, Hall, Hall and Hausman (BHHH) (Berndt et al., 1974) algorithm has been used in most cases, but the Marquardt algorithm is used when the BHHH algorithm does not converge. Several different sets of initial values have been used in each case, but do not lead to a substantial difference in the estimates.

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6.2. Univariate models The univariate AR(1)– GARCH(1,1) and AR(1)– GJR(1,1) models are used to estimate the economic, ﬁnancial, political and composite risk returns and volatilities for the 120 countries using monthly data. Univariate GARCH(1,1) and GJR(1,1) estimates for each country by risk return are reported in Tables 6.1 – 6.8. The univariate estimates for the 16 equations associated with the four Central and South Asian countries are reported in Table 6.1. Table 6.2 presents the univariate estimates for 68 equations associated with the 17 countries of the East Asia and the Paciﬁc region. The GARCH(1,1) and GJR(1,1) estimates for the 36 equations associated with the nine Eastern Europe countries are given in Table 6.3. Table 6.4 presents the estimation results for 72 equations associated with the Middle East and North Africa region. Tables 6.5 and 6.6 report estimation results for 60 and 40 equations, representing the 15 countries of North and Central America and the 10 countries of South America, respectively. Finally, the estimation results for the 104 equations associated with the 26 Sub-Saharan African countries and the 84 equations associated with the 21 West Europe countries are given in Tables 6.7 and 6.8, respectively. A summary of the univariate GARCH(1,1) and GJR(1,1) volatility estimates associated with the economic, ﬁnancial, political and composite risk returns for the eight regions is reported in Table 6.9. Table 6.10 summarizes the preferred model for each country by risk return. The log-moment and second moment conditions for the GARCH(1,1) model are the empirical versions of conditions (8) and (10), respectively, while the log-moment and second moment conditions for the GJR(1,1) model are the empirical versions of conditions (7) and (9), respectively. In order to calculate the empirical counterparts of the log-moment conditions, the QMLE of the parameters are substituted into (7) and (8), together with the corresponding estimated standardized residuals from the respective models. The second moment conditions in (9) and (10) are evaluated at their respective QMLE. These empirical log-moment and second moment conditions provide practical diagnostic checks of the regularity conditions. Asymptotic and robust t-ratios (see Bollerslev and Wooldridge (1992) for the derivation of the robust standard errors) are reported for the QMLE in Tables 6.1 – 6.8. Although there is no algebraic relationship between the asymptotic and robust t-ratios, it would be expected that the robust t-ratios are generally smaller in absolute value, especially in the presence of extreme observations and outliers. In general, the robust t-ratios are smaller in absolute value than their asymptotic counterparts.

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6.2.1. Central and South Asia region: GARCH(1,1) and GJR(1,1) estimates The estimates for Central and South Asia are given in Table 6.1 and summarized in Table 6.9. For the GARCH(1,1) model, the a and b estimates are positive fractions in 13 and 14 cases, respectively. The second moment condition is satisﬁed in 12 of 16 cases for the four countries and four risk returns, while the log-moment condition is satisﬁed 30 times. Overall, when the second moment condition is not satisﬁed, the log-moment condition ensures that the QMLE are consistent and asymptotically normal in the presence of inﬁnite second moments. The second moment condition is satisﬁed in all cases when the log-moment condition could not be computed. There are two cases, namely Pakistan for political risk returns and Sri Lanka for composite returns, when the second moment was not satisﬁed and the log-moment was not satisﬁed or could not be computed. For the GJR(1,1) model, only 6 of the 16 g estimates are signiﬁcant. While the average short-run persistence, a þ g=2; is a positive fraction in 13 cases, the b estimates are positive in 15 cases. The second moment condition is satisﬁed 11 times, but the log-moment condition is satisﬁed only eight times. There is only one case, namely Sri Lanka for composite risk returns, when the consistency and the asymptotic normality of the QMLE are not guaranteed. In this case, the log-moment condition could not be computed and the second moment condition was not satisﬁed. Except for Sri Lanka for composite risk returns, when the second moment condition is not satisﬁed, the log-moment ensures that the QMLE are consistent and asymptotically normal in the presence of inﬁnite second moments. Moreover, the second moment condition is satisﬁed for all cases when the log-moment condition could not be computed. Overall, as only six g estimates were signiﬁcant, the GARCH(1,1) estimates are preferred to their GJR(1,1) counterparts. Table 6.10 reports the preferred model for the 120 representative countries by risk return. As in the discussion above, at the univariate level, the GARCH(1,1) model is generally preferred to its GJR(1,1) counterpart for the Central and South Asia region. For economic risk returns, GARCH(1,1) is superior to GJR(1,1) for Bangladesh, Pakistan and Sri Lanka, while neither model is suitable for India. For ﬁnancial risk returns, the GARCH(1,1) model is preferable for all countries, apart from Pakistan, for which the GJR(1,1) model is preferable. Unlike economic and ﬁnancial risk returns, neither model is favoured for the political risk returns for Pakistan and Sri Lanka. The GARCH(1,1) model is preferable for Bangladesh and the GJR(1,1) model is preferable for India. The GJR(1,1) model generally outperforms the GARCH(1,1) model for the composite

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risk returns. Bangladesh and India favour GJR(1,1), Pakistan favours GARCH(1,1) and neither model is preferred by Sri Lanka. Overall, for Central and South Asia, GARCH(1,1) is the appropriate model in eight cases, GJR(1,1) is the appropriate model in four cases and neither model is preferred in four cases. 6.2.2. East Asia and the Paciﬁc region: GARCH(1,1) and GJR(1,1) estimates The estimates for East Asia and the Paciﬁc are given in Table 6.2 and summarized in Table 6.9. For the GARCH(1,1) model, the a and b estimates are positive fractions in 45 and 58 cases, respectively. The second moment condition is satisﬁed in 55 of 68 cases for the 17 countries and four risk returns, while the log-moment condition is satisﬁed 51 times. When the second moment condition is not satisﬁed, the log-moment condition ensures that the QMLE are consistent and asymptotically normal in the presence of inﬁnite second moments. The second moment condition is satisﬁed in all cases when the log-moment condition could not be computed. For the GJR(1,1) model, only 18 of the 68 g estimates are signiﬁcant. While the average short-run persistence, a þ g=2; is a positive fraction in 42 cases, the b estimates are positive in 65 cases. The second moment condition is satisﬁed 56 times, but the log-moment condition is satisﬁed only 31 times. There are six cases when the consistency and the asymptotic normality of the QMLE are not guaranteed, namely when the log-moment condition is positive or could not be computed, and the second moment condition is not satisﬁed. Generally, apart from six cases, when the second moment condition is not satisﬁed, the log-moment ensures that the QMLE are consistent and asymptotically normal in the presence of inﬁnite second moments. Moreover, apart from six cases, the second moment condition is satisﬁed for all cases when the log-moment condition could not be computed. Overall, as only 18 g estimates were signiﬁcant, and the conditions for the QMLE to be consistent and asymptotic normal could not be ensured in all cases, the GARCH(1,1) estimates are preferred to their GJR(1,1) counterparts. Table 6.10 reports the preferred model for the 120 representative countries by risk return. As in the discussion above, at the univariate level, the GARCH(1,1) model is generally preferred to its GJR(1,1) counterpart for the East Asia and the Paciﬁc region. For economic risk returns, GARCH(1,1) is superior to GJR(1,1) for Australia, Hong Kong, Malaysia, New Zealand, Philippines, South Korea, Taiwan, Thailand and Vietnam. The GJR(1,1) model is preferred only for Brunei and Singapore, while

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neither model is suitable for China, Indonesia, Japan, Mongolia, North Korea and Papua New Guinea. For ﬁnancial risk returns, the GARCH(1,1) model is preferable for all countries, apart from Japan and South Korea, for which the GJR(1,1) model is preferable, and North Korea, Malaysia and Vietnam, for which neither model is suitable. In the case of Australia, New Zealand and Thailand, GARCH(1,1) is superior to GJR(1,1), even though the three g estimates are found to be signiﬁcant. However, for South Korea, GJR(1,1) is superior to GARCH(1,1), even though the g estimate is insigniﬁcant. Unlike economic and ﬁnancial risk returns, GJR(1,1) generally outperforms GARCH(1,1) for political risk returns. The GJR(1,1) model is preferable for Australia, Japan, Mongolia, New Zealand, Taiwan and Thailand, even though the g estimates are insigniﬁcant for all countries, apart from New Zealand. Moreover, while the appropriate model for China, Indonesia, Malaysia, Papua New Guinea and South Korea is GARCH(1,1), neither model is favoured for political risk returns for Brunei, Hong Kong, North Korea, Philippines, Singapore and Vietnam. As for economic and ﬁnancial risk returns, GARCH(1,1) is superior to GJR(1,1) for composite risk returns for Australia, Brunei, Hong Kong, Indonesia, Japan, Mongolia, New Zealand and South Korea. The GJR(1,1) model is preferred for China, Philippines, Singapore and Taiwan, even though the g estimates are insigniﬁcant for all countries, apart from Singapore. Moreover, neither model is preferred for North Korean, Malaysia, Thailand and Vietnam. Overall, for the East Asia and the Paciﬁc region, GARCH(1,1) is the appropriate model in 35 cases, GJR(1,1) is the appropriate model in 15 cases, and neither model is preferred in 18 cases. 6.2.3. East Europe region: GARCH(1,1) and GJR(1,1) estimates The GARCH(1,1) and GJR(1,1) estimates for East Europe are given in Table 6.3 and summarized in Table 6.9. For the GARCH(1,1) model, the a and b estimates are positive fractions in 25 and 31 cases, respectively. The second moment condition is satisﬁed in 29 of 36 cases for the nine countries and four risk returns, while the log-moment condition is satisﬁed 19 times. The consistency and asymptotic normality of the QMLE are not guaranteed for ﬁnancial risk returns for Yugoslavia, as neither the second moment condition nor the log-moment condition is satisﬁed. Apart from this case, when the second moment condition is not satisﬁed, the logmoment condition ensures that the QMLE are consistent and asymptotically normal in the presence of inﬁnite second moments. Similarly, with the exception of ﬁnancial risk returns for Yugoslavia, the second moment

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condition is satisﬁed in all cases when the log-moment condition is either not satisﬁed or could not be computed. For the GJR(1,1) model, only 8 of the 36 g estimates are signiﬁcant. While the average short-run persistence, a þ g=2; is a positive fraction in 26 cases, the b estimates are positive in 34 cases. The second moment condition is satisﬁed 30 times, but the log-moment condition is satisﬁed only 18 times. There is only one case, namely political risk returns for Bulgaria, when the QMLE are not guaranteed to be consistent and asymptotically normal as the second moment condition is not satisﬁed and the log-moment condition could not be computed. Apart from this case, the QMLE are consistent and asymptotically normal as the log-moment is satisﬁed for all cases when the second moment condition is not, while the second moment condition is satisﬁed for all cases when the log-moment condition could not be computed. Overall, the GARCH(1,1) estimates are preferred to their GJR(1,1) counterparts as only eight g estimates were signiﬁcant. The preferred model for the nine East Europe countries by risk return is reported in Table 6.10. As discussed above, at the univariate level, the GARCH(1,1) model is generally preferred to its GJR(1,1) counterpart for the East Europe region. For economic risk returns, GARCH(1,1) is superior to GJR(1,1) for Albania, Hungary, Poland, Slovakia and Yugoslavia. The GJR(1,1) model is preferred only for Bulgaria and Romania, even though the g estimate for Bulgaria was insigniﬁcant. Moreover, neither model is suitable for the Czech Republic or Russia. GARCH(1,1) and GJR(1,1) are equally preferred for ﬁnancial risk returns. Bulgaria, Romania, Russia and Slovakia favour GARCH(1,1), even though the g estimate for Slovakia is signiﬁcant. The GJR(1,1) model is favoured by Albania, Hungary, Poland and Yugoslavia, even though the g estimates are insigniﬁcant for all countries, apart from Albania. However, neither model is appropriate for the Czech Republic. Unlike economic and ﬁnancial risk returns, GJR(1,1) generally outperforms GARCH(1,1) for political risk returns. While neither model is suitable for Bulgaria, Hungary, Russia and Slovakia, GJR(1,1) is preferred for the Czech Republic, Poland and Yugoslavia, even though the g estimate is signiﬁcant only for the Czech Republic. The appropriate model for the two remaining countries, namely Albania and Romania, is GARCH(1,1). As in the case of economic risk returns, GARCH(1,1) is generally superior to GJR(1,1) for composite risk returns. The GARCH(1,1) model is preferred for Albania, Czech Republic, Romania, Russia and Yugoslavia, while GJR(1,1) model is preferred for Bulgaria and Slovakia, even though the g estimate for Slovakia is insigniﬁcant. Neither model is suitable for Hungary and Poland.

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Overall, for the East Europe region, GARCH(1,1) is the preferred model in 16 cases, GJR(1,1) is the preferred model in 11 cases and neither model is preferred in nine cases. 6.2.4. Middle East and North Africa region: GARCH(1,1) and GJR(1,1) estimates The estimates for Middle East and North Africa are given in Table 6.4 and summarized in Table 6.9. For the GARCH(1,1) model, the a and b estimates are positive fractions in 50 and 56 cases, respectively, while the second moment and log-moment conditions are satisﬁed 41 and 56 times, respectively. There are six cases, namely Bahrain for political risk returns, Iraq for economic risk returns, Jordan for political and composite risk returns, Libya for composite risk returns and Oman for political risk returns, when the second moment condition is not satisﬁed and the logmoment condition could not be computed. Except for these six cases, the consistency and asymptotic normality of the QMLE are guaranteed, even in the presence of inﬁnite second moments. Generally, the log-moment condition is satisﬁed when the second moment condition is not, while the second moment condition is satisﬁed for all cases when the log-moment condition could not be computed. For the GJR(1,1) model, only 11 of the 72 g estimates are signiﬁcant. The average short-run persistence, a þ g=2; and b estimates are positive fractions in 57 and 64 cases, respectively. While the second moment condition is satisﬁed 57 times, the log-moment condition is satisﬁed 37 times. The consistency and asymptotic normality of the QMLE for Bahrain and Jordan are not guaranteed for political risk returns, and economic and composite risk returns, respectively. In these cases, the log-moment condition could not be computed and the second moment condition is not satisﬁed. Except for the three risk returns, the log-moment condition is satisﬁed for all cases when the second moment condition is not, and the second moment condition is satisﬁed for all cases when the logmoment condition could not be computed. Hence, the QMLE are generally consistent and asymptotically normal. Overall, only 11 g estimates were signiﬁcant, and the conditions for the QMLE to be consistent and asymptotically normal could not be ensured in six cases for GARCH(1,1) and three for GJR(1,1). Thus, the GARCH(1,1) results seem superior to their GJR(1,1) counterparts. As in the discussion above, Table 6.10 reports that the GARCH(1,1) model is generally superior to its GJR(1,1) counterpart for the Middle East and North Africa region. For economic risk returns, GARCH(1,1) is preferred for 11 countries, namely Algeria, Bahrain, Egypt, Israel, Jordan,

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Kuwait, Libya, Morocco, Saudi Arabia, Syria and the UAE. Iran, Iraq, Oman, Qatar and Yemen favoured the GJR(1,1) model, even though the g estimate was signiﬁcant only for Iran. Neither model was suitable for Lebanon and Tunisia. For ﬁnancial risk returns, the GARCH(1,1) model is preferred for Algeria, Egypt, Israel, Kuwait, Lebanon, Saudi Arabia, Syria and the UAE. The GJR(1,1) model is suitable only for two countries, namely Iraq and Yemen, even though the g estimates were insigniﬁcant. Moreover, neither model is suitable for eight countries, namely Bahrain, Iran, Jordan, Libya, Morocco, Oman, Qatar and Tunisia. Unlike economic and ﬁnancial risk returns, GJR(1,1) generally outperforms GARCH(1,1) for political risk returns. The GJR(1,1) model is preferable for Algeria, Egypt, Jordan, Lebanon, Libya, Morocco, Oman, Saudi Arabia and Syria. However, the g estimates for Jordan, Libya, Morocco and Oman are insigniﬁcant. While neither model is favoured for Bahrain, Qatar and Yemen, the appropriate model for Iran, Iraq, Israel, Kuwait, Tunisia and the UAE is GARCH(1,1). As for economic and ﬁnancial risk returns, GARCH(1,1) is generally superior to GJR(1,1) for composite risk returns. Algeria, Egypt, Israel, Kuwait, Lebanon, Saudi Arabia and Tunisia favour GARCH(1,1), while Bahrain, Libya, Morocco, Syria, UAE and Yemen favour GJR(1,1). Only the g estimate for Syria is signiﬁcant. Moreover, neither model is preferred for Iran, Iraq, Jordan, Oman and Qatar. Overall, for the Middle East and North Africa region, the GARCH(1,1) model is suitable in 32 cases, GJR(1,1) model is suitable in 22 cases, and neither model is preferred in 18 cases. 6.2.5. North and Central America region: GARCH(1,1) and GJR(1,1) estimates The estimates for North and Central America are given in Table 6.5 and summarized in Table 6.9. For the GARCH(1,1) model, the a and b estimates are positive fractions in 36 and 48 cases, respectively. The second moment condition is satisﬁed in 52 of 60 cases for the 15 countries and four risk returns, and the log-moment condition is satisﬁed 48 times. The consistency and asymptotic normality of the QMLE for Honduras and Jamaica are not guaranteed for ﬁnancial risk returns and composite risk returns, respectively, when the log-moment condition could not be computed and the second moment condition is not satisﬁed. Except for these two cases, while the log-moment condition is satisﬁed when the second moment condition is not, the second moment condition is satisﬁed

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for all cases when the log-moment condition could not be computed. Hence, the QMLE are consistent and asymptotically normal. For the GJR(1,1) model, only 7 of the 60 g estimates are signiﬁcant. The average short-run persistence, a þ g=2; and b estimates are positive fractions in 42 and 54 cases, respectively. While the second moment condition is satisﬁed 54 times, the log-moment condition is satisﬁed only 34 times. Apart from ﬁnancial risk returns for Honduras and composite risk returns for the USA, the log-moment condition is satisﬁed for all cases when the second moment condition is not, and the second moment condition is satisﬁed for all cases when the log-moment condition could not be computed. Hence, except for the two cases, the QMLE are consistent and asymptotically normal. Overall, as only seven g estimates were signiﬁcant, the GARCH(1,1) model is superior to its GJR(1,1) counterpart. As in the discussion above, Table 6.10 reports that the GARCH(1,1) model is generally superior to its GJR(1,1) counterpart for the North and Central America region. For economic risk returns, GARCH(1,1) is preferred for the Bahamas, Canada, Costa Rica, Mexico, Panama and the USA, while neither model is suitable for the Dominican Republic. The GJR(1,1) model is preferred for Cuba, Guatemala, Haiti, Honduras, Jamaica, Nicaragua and Trinidad and Tobago, even though only the g estimates for Guatemala, Haiti and Nicaragua are signiﬁcant. For ﬁnancial risk returns, GARCH(1,1) is preferred for Costa Rica, Cuba, El Salvador, Jamaica, Trinidad and Tobago and the USA, while GJR(1,1) is preferred for the Bahamas, the Dominican Republic, Guatemala, Haiti and Nicaragua, even though all the g estimates were insigniﬁcant. Neither model is adequate for Canada, Honduras, Mexico and Panama. The GARCH(1,1) model for political risk returns is preferred for six countries, namely El Salvador, Honduras, Mexico, Nicaragua and the USA, even though g estimate for Nicaragua is signiﬁcant. Neither model is suitable for Canada, Costa Rica, Cuba, the Dominican Republic, Guatemala, Haiti, Jamaica and Trinidad and Tobago, while GJR(1,1) is preferred for the Bahamas, even though the g estimate is insigniﬁcant. Similarly, for composite risk returns, GARCH(1,1) outperforms GJR(1,1) for Costa Rica, Haiti, Honduras, Panama, Trinidad and Tobago and the USA. While neither model is favoured by the Bahamas, Canada, Cuba, the Dominican Republic, Guatemala and Mexico, GJR(1,1) is preferred for El Salvador, Jamaica and Nicaragua. However, the g estimate for Jamaica is insigniﬁcant. Overall, the GARCH(1,1) and GJR(1,1) models are frequently not suitable for political and composite returns for the North and Central America region. Of the 60 cases for the four countries and four risk returns,

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GARCH(1,1) is suitable in 24 cases, GJR(1,1) in 17 cases and neither model is suitable in 19 cases. 6.2.6. South America region: GARCH(1,1) and GJR(1,1) estimates The estimates for the South America region are given in Table 6.6 and summarized in Table 6.9. For the GARCH(1,1) model, the a and b estimates are positive fractions in 36 and 40 cases, respectively. The second moment condition is satisﬁed in 37 of 40 cases for the 10 countries and four risk returns, while the log-moment condition is satisﬁed in 38 cases. Overall, the log-moment condition is satisﬁed for all cases when the second moment condition is not, and the second moment condition is satisﬁed for all cases when the log-moment condition could not be computed. Hence, the QMLE are consistent and asymptotically normal. For the GJR(1,1) model, only 6 of the 40 g estimates are signiﬁcant, while both the average short-run persistence, a þ g=2; and b estimates are positive in 37 cases. Moreover, while the second moment condition is satisﬁed 35 times, the log-moment condition is satisﬁed 28 times. There is only one case, namely Bolivia for composite risk returns, when the second moment condition is not satisﬁed and the log-moment condition could not be computed. Except for composite risk returns for Bolivia, the log-moment condition is satisﬁed for all cases when the second moment condition is not, and the second moment condition is satisﬁed for all cases when the log-moment condition could not be computed. Hence, the QMLE are consistent and asymptotically normal. Overall, as only six g estimates were signiﬁcant, the GARCH(1,1) model is superior to its GJR(1,1) counterpart. As in the discussion above, Table 6.10 reports that GARCH(1,1) is the strongly preferred model for the South America region. For economic risk returns, the GARCH(1,1) model is preferred for six countries, namely Argentina, Bolivia, Brazil, Chile, Colombia and Paraguay. Neither model is suitable for Ecuador, while GJR(1,1) is favoured by Peru, Uruguay and Venezuela, even though the g estimate for Uruguay is insigniﬁcant. The GARCH(1,1) model is preferred for all countries for ﬁnancial risk returns, apart from Uruguay, for which the g estimate was insigniﬁcant, but the GJR(1,1) estimates were generally superior to their GARCH(1,1) counterparts. Similarly, GARCH(1,1) is preferred for all countries for composite risk returns, except for Argentina and Chile, for which GJR(1,1) was favoured, even though the g estimate for Chile was insigniﬁcant. For composite risk returns, GARCH(1,1) is favoured for 8 of the 10 countries, namely Argentina, Bolivia, Brazil, Chile, Paraguay, Peru,

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Uruguay and Venezuela, with a signiﬁcant g estimate for Bolivia. The GJR(1,1) model is preferred only by Colombia and Ecuador. Overall, univariate volatility models are suitable for 39 of the 40 cases for the 10 countries and four risk returns, with GARCH(1,1) being preferred in 31 cases and GJR(1,1) in eight cases. 6.2.7. Sub-Saharan Africa region: GARCH(1,1) and GJR(1,1) estimates The estimates for Sub-Saharan Africa are given in Table 6.7 and summarized in Table 6.9. For the GARCH(1,1) model, the a and b estimates are positive fractions in 70 and 91 cases, respectively. The second moment condition is satisﬁed in 84 of 104 cases for the 26 countries and four risk returns, while the log-moment condition is satisﬁed 74 times. There are ﬁve cases, namely Burkina Faso, Coˆte d’Ivoire and Guinea for composite risk returns, and Ethiopia and Kenya for ﬁnancial risk returns, when the QMLE are not guaranteed to be consistent and asymptotically normal. In each case, the second moment is not satisﬁed and the log-moment could not be computed. Except for the ﬁve risk returns, when the second moment condition is not satisﬁed, the log-moment condition ensures that the QMLE are consistent and asymptotically normal in the presence of inﬁnite second moments. Moreover, apart from ﬁve cases, the second moment condition is satisﬁed in all cases when the log-moment condition could not be computed. For the GJR(1,1) model, only 27 of the 104 g estimates are signiﬁcant. While the average short-run persistence, a þ g=2; is a positive fraction in 69 cases, the b estimates are positive in 96 cases. The second moment condition is satisﬁed 82 times, but the log-moment condition is satisﬁed only 53 times. There are seven cases when the consistency and the asymptotic normality of the QMLE is not guaranteed, namely when the log-moment condition is positive or could not be computed, and the second moment condition is not satisﬁed. Generally, apart from seven cases, when the second moment condition is not satisﬁed, the log-moment ensures that the QMLE are consistent and asymptotically normal in the presence of inﬁnite second moments. Moreover, apart from seven cases, the second moment condition is satisﬁed for all cases when the log-moment condition could not be computed. Overall, as only 27 g estimates were signiﬁcant and the conditions for the QMLE to be consistent and asymptotic normal could not be ensured in all cases, the GARCH(1,1) estimates are preferred to their GJR(1,1) counterparts. Table 6.10 reports the preferred model for the 120 representative countries by risk return. As in the discussion above, at the univariate level,

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the GARCH(1,1) model is generally preferred to its GJR(1,1) counterpart for the Sub-Saharan region. For economic risk returns, GARCH(1,1) is superior to GJR(1,1) for 17 of the 26 countries, even though the g estimates for Kenya and South Africa are signiﬁcant. The GJR(1,1) model is preferred in ﬁve cases, namely Burkina Faso, Congo, Guinea, Malawi and Mali, with insigniﬁcant g estimates for Malawi and Mali. Neither model is suitable for Botswana, Coˆte d’Ivoire, the Democratic Republic of Congo and Liberia. For ﬁnancial risk returns, the GARCH(1,1) model is preferable for 13 countries, while neither model is suitable for 10 countries. The GJR(1,1) model is favoured only by Gabon, Zambia and Zimbabwe. As for ﬁnancial risk returns, GARCH(1,1) generally outperforms GJR(1,1) for political risk returns, with 10 of the 26 countries favouring neither model. The GARCH(1,1) model is preferable for 10 countries, namely Angola, Botswana, Ethiopia, Gabon, Liberia, Mozambique, Nigeria, Sierra Leone, Sudan and Zimbabwe, even though the g estimate for Ethiopia is signiﬁcant. Only six countries, namely Cameroon, Congo, Guinea, Mali, Tanzania and Uganda, favoured GJR(1,1). However, the g estimates were signiﬁcant only for Guinea and Uganda. Unlike the cases given above, GJR(1,1) is superior to GARCH(1,1) for composite risk returns, with 7 of the 26 countries favouring neither model. The GJR(1,1) estimates are superior to their GARCH(1,1) counterparts for 10 countries, even though the g estimates for Botswana, Malawi, Mozambique and Tanzania are insigniﬁcant. However, GARCH(1,1) is preferred by nine countries, namely Angola, Congo, the Democratic Republic of Congo, Gabon, Ghana, Liberia, Sierra Leone, Sudan and Togo. Overall, the GARCH(1,1) and GJR(1,1) models are frequently not suitable for ﬁnancial, political and composite returns for the Sub-Saharan Africa region. However, of the 104 cases, GARCH(1,1) is the appropriate model 49 times, GJR(1,1) is the appropriate model 24 times and neither model is preferred 31 times. 6.2.8. West Europe region: GARCH(1,1) and GJR(1,1) estimates The GARCH(1,1) and GJR(1,1) estimates for West Europe are given in Table 6.8 and summarized in Table 6.9. For the GARCH(1,1) model, the a and b estimates are positive fractions in 56 and 66 cases, respectively. The second moment condition is satisﬁed in 70 of 84 cases for the 21 countries and four risk returns, while the log-moment condition is satisﬁed 58 times. There are eight cases for which the consistency and asymptotic normality of the QMLE are not guaranteed, namely when the second moment condition is not satisﬁed or when the log-moment condition could not be computed or is not satisﬁed. However, apart from eight cases when the second moment

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condition is not satisﬁed, the log-moment condition ensures that the QMLE are consistent and asymptotically normal in the presence of inﬁnite second moments. Similarly, apart from eight cases, the second moment condition ensures that the QMLE are consistent and asymptotically normal for all cases when the log-moment condition could not be computed. For the GJR(1,1) model, only 19 of the 84 g estimates are signiﬁcant. While the average short-run persistence, a þ g=2; is a positive fraction in 61 cases, the b estimates are positive in 72 cases. The second moment condition is satisﬁed 72 times, but the log-moment condition is satisﬁed only 45 times. There are ﬁve cases when the QMLE are not guaranteed to be consistent and asymptotically normal as the second moment condition is not satisﬁed and the log-moment condition could not be computed or is not satisﬁed. Thus, except for ﬁve cases, the QMLE are consistent and asymptotically normal as the log-moment is satisﬁed when the second moment condition is not, while the second moment condition is satisﬁed when the log-moment condition could not be computed or is not satisﬁed. Overall, the consistency and asymptotic normality of the QMLE are not ensured for seven cases for GARCH(1,1) and ﬁve for GJR(1,1), while only 19 of the 80 g estimates are signiﬁcant. Hence, GARCH(1,1) estimates are generally preferred to their GJR(1,1) counterparts. The preferred model for the 21 West Europe countries by risk return is reported in Table 6.10. As discussed above, at the univariate level, the GARCH(1,1) model is generally preferred to its GJR(1,1) counterpart for the West Europe region. For economic risk returns, GARCH(1,1) is superior to GJR(1,1) for Austria, Denmark, Iceland, Ireland, Netherlands, Norway, Portugal, Spain, Switzerland and UK, even though the g estimates for Austria and Norway are signiﬁcant. The GJR(1,1) model is preferred only for ﬁve countries, namely Cyprus, Finland, France, Luxemburg and Turkey, even though the g estimates for Luxembourg and Turkey are insigniﬁcant. Moreover, neither model is suitable for Belgium, Germany, Greece, Italy, Malta and Sweden. Unlike economic risk returns, GJR(1,1) generally outperforms GARCH(1,1) for ﬁnancial risk returns. The GJR(1,1) model is preferred for seven countries, namely Cyprus, Finland, Ireland, Italy, Spain, Sweden and UK, even though the g estimates for Cyprus and UK are insigniﬁcant. Although the g estimate for Luxembourg is signiﬁcant, GARCH(1,1) is superior for Luxembourg, as well as for Iceland, Netherlands, Norway and Switzerland. However, neither model is appropriate for nine countries, namely Austria, Belgium, Denmark, France, Germany, Greece, Malta, Portugal and Turkey. As in the case of economic risk returns, GARCH(1,1) is superior to GJR(1,1) for political risk returns. The appropriate model for Finland,

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France, Germany, Italy, Malta, Netherlands, Norway, Spain and Turkey is GARCH(1,1), even though the g estimate for the Netherlands is signiﬁcant. While neither model is suitable for Austria, Cyprus, Denmark, Greece and Iceland, GJR(1,1) is preferred for the remaining seven countries, namely Belgium, Ireland, Luxembourg, Portugal, Sweden, Switzerland and UK. However, the g estimate is signiﬁcant only for Ireland. In a similar manner to economic and political risk returns, GARCH(1,1) is generally superior to GJR(1,1) for composite risk returns. The GARCH(1,1) model is preferred for 10 countries, namely Belgium, Finland, Iceland, Ireland, Italy, Netherlands, Norway, Portugal, Spain and Turkey. However, the g estimate for Italy is signiﬁcant. Austria, Cyprus, Denmark, Germany, Greece and Switzerland favour the GJR(1,1) model, even though the g estimates for Cyprus, Denmark and Germany are insigniﬁcant. Moreover, neither model is suitable for France, Luxembourg, Malta, Turkey and UK. Overall, for the West Europe region, GARCH(1,1) is the preferred model in 34 cases, GJR(1,1) is preferred in 25 cases and neither model is preferred in 25 cases. 6.3. Multivariate models: static conditional correlations Based on the monthly standardized residuals of the univariate AR(1)– GARCH(1,1) model, the corresponding static conditional correlations can be calculated for the economic, ﬁnancial, political and composite risk return shocks for the 120 countries. As discussed in Chapter 5, the CCC (Bollerslev, 1990), VARMA– GARCH (Ling and McAleer, 2003) and VARMA– AGARCH (Hoti et al., 2002) models can be used to calculate the conditional correlations. As 120 countries will be used to obtain estimates of the conditional correlations, for purposes of simplicity, a restricted case of the above models will be used, namely AR(1)– GARCH(1,1). The estimated conditional correlation matrices for each country by risk return across different regions are reported in Tables 6.11– 6.18. Table 6.11 presents the estimated conditional correlation matrix for the four countries of the Central and South Asia region, while Table 6.12 presents the estimated conditional correlation matrix for the 17 countries of the East Asia and the Paciﬁc region. The estimated conditional correlation matrix for the nine East Europe countries is given in Table 6.13. Table 6.14 presents the estimated conditional correlation matrix for the 18 countries of the Middle East and North Africa region. Tables 6.15 and 6.16 report the estimated conditional correlation matrix for the 15 countries of North and Central America and the 10 countries of South America, respectively. Finally, the estimated conditional correlation matrices for the 26

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Sub-Saharan Africa countries and the 21 West Europe countries are given in Tables 6.17 and 6.18, respectively. A summary of the range of variation of the estimated conditional correlation matrices for the eight regions by risk return is reported in Table 6.19. Moreover, Tables 6.20– 6.27 report the number of the estimated static conditional correlations in given ranges for the eight regions by risk return. 6.3.1. Central and South Asia region: static conditional correlation estimates The GARCH(1,1) static conditional correlation estimates for four Central and South Asia countries by risk return are given in Table 6.11 and summarized in Tables 6.19 and 6.20. As reported in Table 6.11, the conditional correlations vary in size and are generally positive across the four risk returns. Table 6.19 shows that the range of the estimated correlations varies across the four risk returns. With a range of (20.084, 0.258), the conditional correlations for economic risk returns vary the most. This is followed by ﬁnancial risk returns, with a range of (20.050, 0.259), composite risk returns with (0.093, 0.328) and political risk returns with (0.025, 0.245). Overall, the largest conditional correlation of 0.328 holds for political risk returns, while the smallest conditional correlation of 20.084 applies to economic risk returns. Table 6.20 reports the number of static conditional correlations in given ranges by risk return. For economic risk returns, only two of the six conditional correlations exceed 0.200. Thus, the conditional correlations for economic risk returns for Central and South Asia are very low. The two largest conditional correlations are for (India, Pakistan) and (Bangladesh, Sri Lanka), the only two correlated country pairs in the region. The static conditional correlations for ﬁnancial risk returns are also very low. Only one of the six conditional correlations exceeds 0.200, namely (Bangladesh, India). Thus, independent effects are observed for all country pairs, except for (Bangladesh, India). Unlike the cases of economic and ﬁnancial risk returns, the conditional correlations for political and composite risk returns are generally higher. Three of the six conditional correlations exceed 0.200 for both political and composite risk returns. The largest conditional correlations for the political and composite risk returns are for (India, Sri Lanka) and (India, Pakistan), respectively. Overall, slightly stronger conditional correlations hold for political and composite risk returns. The largest range of variation is for economic risk returns, followed by ﬁnancial, composite and political risk returns.

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6.3.2. East Asia and the Paciﬁc region: static conditional correlation estimates The GARCH(1,1) static conditional correlation estimates for 21 East Asia and the Paciﬁc countries by risk return are given in Table 6.12 and summarized in Tables 6.19 and 6.21. As reported in Table 6.12, the conditional correlations vary in size and are generally positive across the four risk returns. Only 30 of the 136 conditional correlations are negative. Moreover, Table 6.19 shows that the range of the estimated correlations varies across the four risk returns. With a range of (20.475, 0.608), the conditional correlations for ﬁnancial risk returns vary the most, followed by composite risk returns with (20.296, 0.451), economic risk returns with (2 0.270, 0.431) and political risk returns with (20.177, 0.341). The largest conditional correlation of 0.608 holds for ﬁnancial risk returns. Table 6.21 reports the number of GARCH(1,1) static conditional correlation coefﬁcients in given ranges by risk return. For economic risk returns, 102 of the 136 conditional correlations are less than 0.200 or higher than 20.200. Of the remaining 32 conditional correlations, 2 range from (2 0.300, 20.201), 18 range from (0.200, 0.299), 11 from (0.300, 0.399) and 1 from (0.400, 0.499). The largest conditional correlation is for (Japan, Malaysia). For GARCH(1,1), the highest ﬁve conditional correlations that range from (0.300, 0.399) are (Hong Kong, Indonesia), (Philippines, Indonesia), (North Korea, Indonesia), (Malaysia, Indonesia) and (Malaysia, Thailand). Three quarters of the conditional correlations are close to zero for economic risk returns. Overall, 15 of the 17 countries have correlations that exceed 0.200 in absolute terms with one or more countries. China and Papua New Guinea are the only two countries in the region with independent effects. The static conditional correlations for ﬁnancial risk returns are generally very high. Only 51 of the 136 GARCH(1,1) conditional correlations are less than 0.200 in absolute value. Of the remaining 85 conditional correlations, 2 range from (2 0.500, 20.401), 9 from (2 0.400, 20.301), 10 from (20.300, 20.201), 15 from (0.200, 0.299), 20 from (0.300, 0.399), 16 from (0.400, 0.499), 11 from (0.500, 0.599) and 2 from (0.600, 0.699). The two highest conditional correlations are for (New Zealand, Australia) and (New Zealand, Thailand). South Korea and Vietnam seem to be two segmented countries, since their conditional correlations with the remaining 15 countries in the region are all lower than 0.200 in absolute value. For political risk returns, 126 of the 136 conditional correlations range from (20.200, 0.200). Of the remaining 10 conditional correlations, 9 range from (0.200, 0.299) and 1 from (0.300, 0.399). Thus, only six conditional

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correlations exceed 0.200, with (Malaysia, Singapore) having the highest correlation of 0.341. Given the majority of low conditional correlations, a large number of countries seem to be segmented in the region. The conditional correlations in these cases are all less than 0.200 in absolute value. There are six independent countries for GARCH(1,1), namely Australia, China, Indonesia, North Korea, South Korea and Vietnam. The conditional correlations for composite risk returns are also low, with 110 of the 136 conditional correlations less than 0.200 or higher than 20.200. Of the remaining 26 GARCH(1,1) conditional correlations, 1 ranges from (20.300, 20.201), 19 from (0.200, 0.299), 5 from (0.300, 0.399) and 1 from (0.400, 0.499). The highest correlation is for (Indonesia, Malaysia), followed by (South Korea, Malaysia) and (Japan, Malaysia). Moreover, North Korea, Papua New Guinea and Singapore seem to be segmented countries, as their conditional correlations with the remaining countries within the region are all less than 0.200 in absolute value. Overall, the strongest conditional correlations and the largest range of variation are for ﬁnancial risk returns, followed by composite, economic and political risk returns. Independent effects for various countries are observed for all risk returns, particularly for political and composite returns. China seems to be independent for economic and political risk returns, South Korea and Vietnam for ﬁnancial and political risk returns, North Korea for political and composite risk returns, Papua New Guinea for economic and composite risk returns, Australia for political risk returns, Indonesia for political risk returns and Singapore for composite risk returns. 6.3.3. East Europe region: static conditional correlation estimates The GARCH(1,1) static conditional correlation estimates for nine East Europe countries by risk return are given in Table 6.13 and are summarized in Tables 6.19 and 6.22. As reported in Table 6.13, the conditional correlations vary in size and are generally positive across the four risk returns. However, Russia for ﬁnancial risk returns and Yugoslavia for economic and composite risk returns tend to be negatively correlated with other countries in the region. Table 6.19 shows that the range of the estimated correlations varies across the four risk returns. With a range of (2 0.391, 0.737), the conditional correlations for ﬁnancial risk returns vary the most. This is followed by economic risk returns with a range of (2 0.218, 0.475), composite risk returns with a range of (20.077, 0.511) and political risk returns with a range of (2 0.106, 0.454). The largest conditional correlation of 0.737 holds for ﬁnancial risk returns. Table 6.17 reports the number of GARCH(1,1) static conditional correlations in given ranges by risk return. For economic risk returns, 24 of

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the 36 conditional correlations are less than 0.200 in absolute value. Of the remaining one-third, 1 ranges from (20.300, 20.201), 6 from (0.200, 0.299), 3 from (0.300, 0.399) and 2 from (0.400, 0.499). The largest conditional correlation is for (Poland, Czech Republic) followed by (Poland, Hungary), (Russia, Czech Republic), (Slovakia, Romania) and (Albania, Bulgaria). Overall, while the majority of conditional correlations is close to 0, all nine East Europe countries have correlations that exceed 0.200 in absolute value with one or more countries. The static conditional correlations for ﬁnancial risk returns can be very high. More than half of the 36 GARCH(1,1) conditional correlations are less than 0.200 or higher than 20.200. Of the remaining 17 conditional correlations, 2 range from (20.400, 20.301), 2 from (0.200, 0.299), 3 from (0.300, 0.399), 5 from (0.400, 0.499), 2 from (0.500, 0.599), 2 from (0.600, 0.699) and 1 from (0.700, 0.799). The highest conditional correlation is for (Hungary, Czech Republic), with the next two highest correlations being (Poland, Czech Republic) and (Slovakia, Hungary). Overall, eight of the nine countries have correlations that exceed 0.200 (in absolute value) with two or more countries. However, Albania is the only country in the region with independent effects. For political risk returns, almost two-thirds of the conditional correlations are close to 0. Of the remaining 15 GARCH(1,1) conditional correlations, 10 range from (0.200, 0.299), 4 from (0.300, 0.399) and 1 from (0.400, 0.499). For the 14 remaining GJR(1,1) conditional correlations, 10 range from (0.200, 0.299), 4 from (0.300, 0.399) and 1 from (0.400, 0.499). Thus, the conditional correlations are generally low across the four risk returns. The highest correlation holds for (Poland, Czech Republic), followed by (Slovakia, Poland) and (Romania, Hungary). Moreover, Russia is the only independent country in the region. As in the case of political risk returns, the conditional correlations for composite risk returns are generally low. Two-thirds of the 36 conditional correlations range from (2 0.100, 0.199). Of the remaining 12 GARCH(1,1) conditional correlations, 8 range from (0.200, 0.299), 2 from (0.300, 0.399), 1 from (0.400, 0.499) and 1 from (0.500, 0.599). The two highest correlations are for (Poland, Hungary) and (Poland, Czech Republic). Moreover, Russia and Yugoslavia seem to be independent, as their conditional correlations with the remaining countries within the region are less than 0.200 in absolute value. Overall, the strongest conditional correlations and the largest range of variation are for ﬁnancial risk returns, followed by economic risk returns, composite risk returns and political risk returns. Independent effects for various countries are observed for all risk returns, particularly for political and composite returns. Russia seems to be independent for political

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and composite risk returns, Albania for ﬁnancial risk returns and Yugoslavia for composite risk returns. 6.3.4. Middle East and North Africa region: static conditional correlation estimates The GARCH(1,1) static conditional correlation estimates for 18 Middle East and North Africa countries by risk return are given in Table 6.14 and summarized in Tables 6.19 and 6.23. As reported in Table 6.14, the conditional correlations vary in size and are generally positive across the four risk returns. Moreover, Table 6.19 shows that the range of the estimated correlations varies across the four risk returns. With a range of (2 0.241, 0.802), the conditional correlations for composite risk returns vary the most. This is followed by political risk returns with a range of (2 0.251, 0.704), ﬁnancial risk returns with (20.189, 0.685) and economic risk returns with (20.302, 0.568). The largest conditional correlation of 0.802 holds for composite risk returns. Table 6.23 reports the number of GARCH(1,1) static conditional correlations in given ranges by risk return. For economic risk returns, 83 of the 153 conditional correlations are less than 0.200 in absolute terms. Of the remaining 70, 1 ranges from (20.400, 20.301), 2 from (20.300, 20.201), 39 from (0.200, 0.299), 17 from (0.300, 0.399), 8 from (0.400, 0.499) and 3 from (0.500, 0.599). The largest conditional correlation of 0.568 is for (Morocco, Tunisia). Overall, 29 of the 153 conditional correlations are negative. Iraq tends to be negatively correlated with the other countries in the region. The static conditional correlations for ﬁnancial risk returns are generally higher than in the case for economic risk returns. For GARCH(1,1), 65 of the 153 conditional correlations have absolute values of less than 0.200. Of the remaining 88 conditional correlations, 1 ranges from (20.300, 20.201), 34 from (0.200, 0.299), 25 from (0.300, 0.399), 18 from (0.400, 0.499), 7 from (0.500, 0.599) and 3 from (0.600, 0.699). The three highest GARCH(1,1) conditional correlations are for (Morocco, Oman), (Morocco, Tunisia) and (Saudi Arabia, UAE). Overall, 21 of the 153 conditional correlations are negative. Bahrain and Iraq tend to be negatively correlated with the other countries in the region. The conditional correlations for political risk returns are very high, with 120 of the 153 exceeding 0.200 in absolute value. Only 13 conditional correlations are negative, all of them for Algeria. Of the 120 GARCH(1,1) conditional correlations, 1 ranges from (20.300, 20.201), 33 from (0.200, 0.299), 44 from (0.300, 0.399), 24 from (0.400, 0.499), 10 from (0.500, 0.599), 7 from (0.600, 0.699) and 1 from (0.700, 0.799). The four largest

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correlations hold for (Saudi Arabia, UAE), (Qatar, UAE), (Morocco, UAE) and (Morocco, Qatar). Overall, strong interdependent effects are observed for all countries in the region. The UAE has the highest conditional correlations while Algeria has the lowest conditional correlations in the region. Conditional correlations for composite risk returns are the strongest in the region, with only 25 of the 153 conditional correlations for GARCH(1,1) less than 0.200 in absolute value. Of the remaining 128 conditional correlations, 1 ranges from (20.300, 20.201), 22 from (0.200, 0.299), 60 from (0.300, 0.399), 32 from (0.400, 0.499), 9 from (0.500, 0.599), 3 from (0.600, 0.699) and 1 from (0.800, 0.899). The highest conditional correlation is for (Morocco, Tunisia), followed by (Kuwait, Saudi Arabia), (Saudi Arabia, UAE) and (Oman, Tunisia). Only eight conditional correlations are negative, all of them for Algeria. Strong interdependent effects are observed for all countries in the region, apart from Algeria. Overall, the strongest conditional correlations and the largest range of variation are for composite risk returns, followed by political, ﬁnancial and economic risk returns. Strong interdependent effects are observed for political and composite risk returns for all countries, apart from Algeria. In particular, the UAE, Saudi Arabia, Qatar, Tunisia, Morocco, Kuwait and Oman are highly correlated with each other. 6.3.5. North and Central America region: static conditional correlation estimates The GARCH(1,1) static conditional correlation estimates for 15 North and Central America countries by risk return are given in Table 6.15 and summarized in Tables 6.19 and 6.24. As reported in Table 6.15, the conditional correlations for GARCH(1,1) vary in size across the four risk returns. The countries tend to be negatively correlated for the economic and ﬁnancial risk returns. Table 6.19 shows that the range of the estimated correlations varies across the four risk returns. With a range of (20.635, 0.602), the conditional correlations for ﬁnancial risk returns vary the most. This is followed by economic risk returns with a range of (20.216, 0.327), composite risk returns with (20.170, 0.351) and political risk returns with (2 0.125, 0.329). Table 6.19 reports the number of GARCH(1,1) static conditional correlations in given ranges by risk return. For economic risk returns, only 8 of the 105 conditional correlations exceed 0.200 in absolute value, while 41 are negative. Thus, the conditional correlations for economic risk returns for North and Central America are very low. The two largest conditional correlations are for (Guatemala, Honduras) and (Honduras,

370

S. Hoti and M. McAleer

Nicaragua). El Salvador, Haiti, Mexico, Panama and the USA generally have independent effects. The GARCH(1,1) static conditional correlations for ﬁnancial risk returns can be very high, even with 53 of the 105 conditional correlations ranging from (20.200, 0.199). Moreover, 49 of the 105 conditional correlations are negative. For the remaining 52 conditional correlations, 2 range from (2 0.700, 20.601), 5 from (20.500, 20.401), 4 from (20.400, 20.301), 10 from (20.300, 20.201), 13 from (0.200, 0.299), 7 from (0.300, 0.399), 7 from (0.400, 0.499), 3 from (0.500, 0.599) and 1 from (0.600, 0.699). The highest (absolute) correlation is negative and holds for (Canada, Guatemala), followed by a positive correlation for (Nicaragua, USA) and a negative correlation for (Guatemala, USA). Bahamas is the only country with independent effects, while the USA is strongly correlated with all the countries in the region, apart from Bahamas. As in the case of economic risk returns, the conditional correlations for political and composite risk returns are very low. However, only 11 and 22 of the 105 conditional correlations are negative for political and composite risk returns, respectively. For political risk returns, 22 correlations exceed 0.200, with 17 ranging from (0.200, 0.299) and 5 from (0.300, 0.399). Only 12 of the conditional correlations exceed 0.200 for composite risk returns, with 10 ranging from (0.200, 0.299) and 2 from (0.300, 0.399). The two largest conditional correlations for political risk returns are (Honduras, Jamaica) and (El Salvador, Nicaragua) and for composite risk returns are (El Salvador, Guatemala) and (Guatemala, Honduras). Based on the conditional correlations, Bahamas has independent effects for composite risk returns, while Cuba and Mexico have independent effects for political and composite risk returns. Overall, strong conditional correlations hold only for ﬁnancial risk returns. The largest range of variation is for ﬁnancial risk returns, followed by economic, composite and political risk returns. Independent effects for various countries are observed for all risk returns. El Salvador, Haiti, Panama and the USA seem to be independent for economic risk returns, Mexico for economic, political and composite risk returns, Bahamas for ﬁnancial and political risk returns and Cuba for political and composite risk returns. 6.3.6. South America region: static conditional correlation estimates The GARCH(1,1) static conditional correlation estimates for 10 South America countries by risk return are given in Table 6.16 and summarized in Tables 6.19 and 6.25. As reported in Table 6.16, the conditional correlations are very low. Of the 10 South American countries, Chile,

Univariate and Multivariate Estimates

371

Paraguay and Uruguay are frequently negatively correlated with the remaining three countries for all risk returns. Table 6.19 shows that the range of the estimated correlations varies across the four risk returns. With a range of (20.222, 0.442), the conditional correlations for ﬁnancial risk returns vary the most. This is followed by composite risk returns, with a range of (20.134, 0.314), political risk returns with (20.060, 0.357) and economic risk returns with (20.103, 0.257). The largest conditional correlation of 0.442 occurs for ﬁnancial risk returns. Table 6.25 reports the number of GARCH(1,1) static conditional correlations in given ranges by risk return. All but two conditional correlations for the South America region are close to zero, with the two ranging from (0.200, 0.299). Of the 45 conditional correlations, 15 are negative. The only two country pairs that seem to be correlated are (Uruguay, Paraguay) and (Uruguay, Chile). Hence, the remaining seven countries seem to be independent of the others for economic risk returns. For ﬁnancial risk returns, only 10 of the 45 conditional correlations for GARCH(1,1) exceed 0.200 in absolute value, with 2 ranging from (2 0.300, 2 0.201), 6 from (0.200, 0.299) and 2 from (0.400, 0.499). The two largest conditional correlations are for (Chile, Uruguay) and (Bolivia, Ecuador). Despite the generally low conditional correlations, Paraguay seems to be the only independent country in the region. As for ﬁnancial risk returns, only 8 of the 45 conditional correlations for political risk returns exceed 0.200, with only ﬁve negative conditional correlations. Of the eight conditional correlations, 7 range from (0.200, 0.299) and 1 from (0.300, 0.399). The largest conditional correlation for political risk returns is for (Argentina, Venezuela). Overall, Uruguay and Venezuela are correlated with Argentina, Bolivia, Chile, Ecuador, Paraguay and each other, while Brazil, Colombia and Peru seem to be independent countries. Like the above, only 4 of the 45 conditional correlations for composite risk returns exceed 0.200 in absolute value, while only nine are negative. Of the four conditional correlations, 3 range from (0.200, 0.299) and 1 from (0.300, 0.399). The highest correlation of 0.314 is for (Chile, Uruguay), followed by (Bolivia, Chile), (Bolivia, Ecuador) and (Paraguay, Uruguay). Hence, Argentina, Brazil, Colombia, Peru and Venezuela seem to be independent countries. Overall, the conditional correlations for the 16 South America countries are all exceedingly low. The highest conditional correlation applies to ﬁnancial risk returns, followed by political, composite and economic risk returns. Financial risk returns have the largest range of variation, followed by composite, political and economic risk returns. Independent effects are observed for all countries, apart from Bolivia, Chile and Uruguay.

372

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Independent effects are observed for Argentina for composite risk returns, Ecuador for economic risk returns, Paraguay for ﬁnancial risk returns, Brazil for political and composite risk returns, Venezuela for economic and composite risk returns, and Colombia and Peru for economic, political and composite risk returns. 6.3.7. Sub-Saharan Africa region: static conditional correlation estimates The GARCH(1,1) static conditional correlation estimates for 26 SubSaharan Africa countries by risk return are given in Table 6.17 and are summarized in Tables 6.19 and 6.26. As reported in Table 6.17, the conditional correlations vary in size and sign across the four risk returns. Moreover, Table 6.19 shows that the range of the estimated correlations for the two models varies across the four risk returns. With a range of (2 0.652, 0.765), the conditional correlations for ﬁnancial risk returns vary the most. This is followed by economic risk returns, with a range of (2 0.301, 0.490), political risk returns with (20.182, 0.555) and composite risk returns with (20.210, 0.352). The largest conditional correlation of 0.765 applies to ﬁnancial risk returns. Table 6.26 reports the number of GARCH(1,1) static conditional correlations in given ranges by risk return. The conditional correlations for economic risk returns are generally very low, with 296 of the 325 conditional correlations less than 0.200 in absolute value. Of the remaining 29, 1 ranges from (20.400, 20.301), 10 from (20.300, 20.201), 12 from (0.200, 0.299), 5 from (0.300, 0.399) and 1 from (0.400, 0.499). The largest conditional correlation is for (Democratic Republic of Congo, South Africa), followed by (Malawi, Tanzania), (Mozambique, South Africa), (Burkina Faso, Democratic Republic of Congo) and (Botswana, Democratic Republic of Congo). Despite the low conditional correlations, only Angola and Guinea seem to have independent effects. The static conditional correlations for ﬁnancial risk returns can be extremely strong, even with 213 of the 325 conditional correlations being less than 0.200 in absolute value. Moreover, 143 of the 325 conditional correlations are negative. Of the 112 high conditional correlations, 1 ranges from (2 0.700, 20.601), 2 from (20.600, 20.501), 10 from (2 0.500, 2 0.401), 12 from (20.400, 2;0.301), 20 from (20.300, 20.201), 28 from (0.200, 0.299), 13 from (0.300, 0.399), 10 from (0.400, 0.499), 7 from (0.500, 0.599), 2 from (0.600, 0.699) and 2 from (0.700, 0.799). The highest conditional correlations are for (Liberia, Mali), followed by (Mali, Senegal) and (Liberia, Sierra Leone). Moreover, Liberia is also highly correlated with Tanzania (20.652) and with

Univariate and Multivariate Estimates

373

Sudan (0.618). Overall, interdependent effects are observed at least in one case for all 26 countries in the region, apart from Guinea. Unlike the case of ﬁnancial risk returns, the conditional correlations for political risk returns are very low, as 264 of the 325 conditional correlations are less than 0.200 in absolute value. Only 46 of the 325 conditional correlations are negative. Of the remaining 61 GARCH(1,1) conditional correlations, 29 range from (0.200, 0.299), 21 from (0.300, 0.399), 8 from (0.400, 0.499) and 3 from (0.500, 0.599). The highest conditional correlation is for (Zambia, Zimbabwe), followed by (Zambia, Botswana) and (Zambia, Ghana). Even though most of the conditional correlations are close to 0, interdependent effects are observed at least in one case for all the countries in the region. The conditional correlations for composite risk returns are much lower than their economic, ﬁnancial and political counterparts. Almost all of the conditional correlations are less than 0.200 in absolute value, while 77 conditional correlations are negative. Of the 28 high GARCH(1,1) conditional correlations, 3 range from (20.300, 20.201), 21 range from (0.200, 0.299) and 5 from (0.300, 0.399). The ﬁve highest conditional correlations are for (South Africa, Zambia), (South Africa, Kenya), (South Africa, Botswana), (South Africa, Zimbabwe) and (Burkina Faso, Liberia). Based on conditional correlations, the Democratic Republic of Congo, Guinea, Mozambique, Sierra Leone and Uganda seem to be independent of the other countries in the region. Overall, the largest range of variation is for ﬁnancial risk returns, followed by economic, political and composite risk returns. The strongest conditional correlations, and hence interdependent effects, are observed for ﬁnancial risk returns. Independent effects hold only for the Democratic Republic of Congo, Guinea, Mozambique, Sierra Leone and Uganda for composite risk returns. 6.3.8. West Europe region: static conditional correlation estimates The GARCH(1,1) static conditional correlation estimates for 21 West Europe countries by risk return are given in Table 6.18 and are summarized in Tables 6.19 and 6.27. As reported in Table 6.18, the conditional correlations vary in size and are generally positive across the four risk returns. Moreover, Table 6.19 shows that the range of the estimated correlations for the two models varies across the four risk returns. With a range of (20.079, 0.843), the conditional correlations for ﬁnancial risk returns vary the most. This is followed by political risk returns with a range of (2 0.147, 0.533), economic risk returns with (2 0.297, 0.350)

374

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and composite risk returns with (20.084, 0.491). The largest conditional correlation of 0.843 applies to ﬁnancial risk returns. Table 6.27 reports the number of GARCH(1,1) static conditional correlations in given ranges by risk return. The conditional correlations for economic risk returns are generally very low. For GARCH(1,1), 174 of the 210 conditional correlations are less than 0.200 in absolute value. Of the remaining 36, 1 ranges from (20.300, 20.201), 25 from (0.200, 0.299) and 10 from (0.300, 0.399). The largest conditional correlation is for (Luxembourg, Germany), followed by (Malta, Spain), (France, Italy), (Belgium, Cyprus) and (Germany, Switzerland). Overall, as most of conditional correlations are close to 0, 4 of the 20 West Europe countries, namely Denmark, Finland, Greece and Iceland, seem to have independent effects. The static conditional correlations for ﬁnancial risk returns are extremely strong. Only 13 of the 210 GARCH(1,1) conditional correlations are less than 0.200 in absolute value. Of the remaining 197 conditional correlations, 14 range from (0.200, 0.299), 20 from (0.300, 0.399), 40 from (0.400, 0.499), 44 from (0.500, 0.599), 42 from (0.600, 0.699), 34 from (0.700, 0.799) and 3 from (0.800, 0899). The highest conditional correlations are for (France, Belgium), followed by (Netherlands, Luxembourg) and (Netherlands, Belgium). Moreover, the weakest conditional correlations are observed between Norway and the other countries in the region. Overall, as more than 90% of the conditional correlations exceed 0.200, strong interdependent effects are observed for all 21 countries in the region. Unlike the case of ﬁnancial risk returns, the conditional correlations for political risk returns are very low, as 188 of the 210 conditional correlations are less than 0.200 in absolute value. Of the remaining 22 GARCH(1,1) conditional correlations, 16 range from (0.200, 0.299), 4 from (0.300, 0.399), 1 from (0.400, 0.499) and 1 from (0.500, 0.599). The highest conditional correlations hold for (Turkey, France) and (Turkey, Malta). Overall, as most of the conditional correlations are close to 0, independent effects are observed for various countries in the region, namely Belgium, Denmark, Germany, Greece, Iceland and Norway. The conditional correlations for composite risk returns are generally higher than their economic and political counterparts. More than one-half of the conditional correlations are less than 0.200 in absolute value. Of the 97 remaining GARCH(1,1) conditional correlations, 65 range from (0.200, 0.299), 22 from (0.300, 0.399) and 10 from (0.400, 0.499). The ﬁve highest conditional correlations apply to (Netherlands, Denmark), (Denmark, Malta), (Cyprus, Austria), (Cyprus, Malta) and (Netherlands, Belgium). Based on conditional correlations, Germany, Greece, Iceland, Ireland,

Univariate and Multivariate Estimates

375

Norway, Turkey and UK are weakly correlated with other countries in the region, with correlations less than 0.300. Overall, the strongest conditional correlations and the largest range of variation are for ﬁnancial risk returns, followed by political, economic and composite risk returns. Independent effects for various countries are observed for economic, political and composite risk returns. Based on the static conditional correlations, Denmark, Greece and Iceland are independent for economic and political risk returns, Finland for economic risk returns, and Belgium, Germany and Norway for political risk returns. 6.4. Summary: static conditional correlation estimates In general, the magnitude and range of the GARCH(1,1) static conditional correlation estimates vary across the eight regions and four risk returns. Denoting the lowest range of variation in the conditional correlations as 1 and the highest as 4, Table 6.8 ranks the conditional correlations for the economic, ﬁnancial, political and composite risk returns for the eight regions. The highest rankings for the range of the conditional correlations for the eight regions always hold for the ﬁnancial, economic or composite risk returns. Conditional correlations for ﬁnancial risk returns are ranked the highest in six regions, namely East Asia and the Paciﬁc, East Europe, North and Central America, South America, Sub-Saharan Africa and West Europe, while those for economic and composite risk returns are ranked the highest for Central and South Asia, and Middle East and North Africa, respectively. Political, economic or composite risk returns always have the lowest rankings for the range of the conditional correlations for the eight regions. For political risk returns, the conditional correlations for Central and South Asia, East Asia and the Paciﬁc, East Europe, and North and Central America, are ranked the lowest. For economic risk returns, the conditional correlations are ranked the lowest for Middle East and North Africa, and South America, while the lowest ranked conditional correlations for composite risk returns are for Sub-Saharan Africa and West Europe. Furthermore, the range of variation of the conditional correlations for ﬁnancial risk returns has a rank of 2 for Middle East and North Africa, and a rank of 3 for Central and South Asia. For economic risk returns, the range of variation of the conditional correlations has a rank of 2 for East Asia and the Paciﬁc, and West Europe, and a rank of 3 for East Europe, North and Central America, and Sub-Saharan Africa. The range of variation of the conditional correlations for political risk returns has a rank of 2 for

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South America and Sub-Saharan Africa, and a rank of 3 for Middle East and North Africa, and West Europe. For composite risk returns, the range of variation of the conditional correlations has a rank of 2 for Central and South Asia, East Europe, and North and Central America, and a rank of 3 for East Asia and the Paciﬁc, and South America. Overall, the rankings by range of variation of the GARCH(1,1) conditional correlations across the eight regions are always the highest for ﬁnancial, economic or composite risk returns, and always the lowest for political, economic or composite risk returns. The rankings by range of variation of the conditional correlations for ﬁnancial risk returns are never the lowest, and those for political risk returns are never the highest. 6.5. Conclusion This chapter provided a comparison of monthly ICRG country risk ratings and risk returns. As risk ratings can be treated as indexes, their rates of change, or risk returns, were analysed in the same manner as ﬁnancial returns. An understanding of the relationship between country risk ratings and risk returns, and the conditional correlations of shocks between pairs of risk returns, are essential for optimal decision making about foreign investments. Three important values of conditional correlations between pairs of shocks to risk returns are as follows: (1) Conditional correlation ¼ 1: As the shocks to the risk returns for the two countries move in the same direction, foreign investors should decide to invest on the basis of the risk ratings of both countries and the country with the higher risk returns. Thus, if both countries have the same risk rating, the foreign investor should choose the country with the higher risk returns. (2) Conditional correlation ¼ 0: As the shocks to the risk returns for the two countries are uncorrelated, foreign investors should decide to invest on the basis of the risk ratings and risk returns of both countries. Thus, if both countries have the same risk returns, the foreign investor should choose the country with the higher risk rating; if both countries have the same risk rating, the foreign investor should choose the country with the higher risk returns. (3) Conditional correlation ¼ 21: As the shocks to the risk returns for the two countries move in opposite directions, foreign investors should hedge and invest in both countries according to their respective risk ratings.

Univariate and Multivariate Estimates

377

The empirical results provided a comparative assessment of the conditional means and volatilities associated with country risk returns across alternative risk returns and countries over time, enabled a validation of the regularity conditions underlying the model, highlighted the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating, and evaluated the usefulness of the ICRG risk ratings. In particular, at the univariate level for both the symmetric GARCH and asymmetric GJR models, the sufﬁcient parametric conditions for the estimated volatilities to be positive were generally satisﬁed, as were the log-moment and second moment conditions for the QMLE to be consistent and asymptotically normal. Based on the monthly standardized residuals of the univariate AR(1)– GARCH(1,1) models, the corresponding static conditional correlations were calculated for the economic, ﬁnancial, political and composite risk return shocks for the 120 countries. Overall, the rankings by range of variation of the GARCH(1,1) static conditional correlations across the eight regions are always the highest for ﬁnancial, economic or composite risk returns, and always the lowest for political, economic or composite risk returns. The rankings by range of variation of the conditional correlations for ﬁnancial risk returns are never the lowest and those for political risk return are never the highest. References Berndt, E.K., B.H. Hall, R.E. Hall and J.A. Hausman (1974), “Estimation and inference in nonlinear structural models”, Annals of Economic and Social Measurement, Vol. 3, pp. 653– 665. Bollerslev, T. (1986), “Generalised autoregressive conditional heteroscedasticity”, Journal of Econometrics, Vol. 31, pp. 307– 327. Bollerslev, T. (1990), “Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH approach”, Review of Economics and Statistics, Vol. 72, pp. 498– 505. Bollerslev, T. and J.M. Wooldridge (1992), “Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances”, Econometric Reviews, Vol. 11, pp. 143– 173. Glosten, L., R. Jagannathan and D. Runkle (1992), “On the relation between the expected value and volatility of nominal excess return on stock”, Journal of Finance, Vol. 46, pp. 1779– 1801. Hoti S., F. Chan and M. McAleer (2002), Structure and asymptotic theory for multivariate asymmetric volatility: empirical evidence for country risk ratings, Invited Paper Presented at the Australasian Meeting of the Econometric Society, Brisbane, Australia, July 2002. Ling, S. and M. McAleer (2003), “Asymptotic theory for a vector ARMA– GARCH model”, Econometric Theory, Vol. 19, pp. 278– 308.

378

Table 6.1. Univariate GARCH(1,1) and GJR(1,1) estimates for Central and South Asia by risk return Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Bangladesh

Economic

Political Composite

India

Economic Financial Political Composite

a

g

b

a þ g/2

Second

2.3 £ 10 2.862 1.493 4.7 £ 1025 6.041 0.523 7.5 £ 1025 3.994 1.512 2.7 £ 1025 3.622 1.046

0.116 2.357 1.553 0.097 3.515 1.429 0.289 4.111 2.822 0.181 4.491 1.849

0.735 9.413 5.303 0.872 39.206 6.111 0.669 10.656 7.185 0.777 21.197 5.956

20.214

0.851

20.066

0.969

20.168

0.958

20.096

0.958

1.2 £ 1024 3.059 1.883 2.6 £ 1025 3.220 0.970 5.9 £ 1025 4.064 1.053 1.8 £ 1025 3.582 0.789

20.044 24.210 23.998 0.095 3.412 1.719 0.165 6.531 1.591 0.127 5.124 1.750

0.865 17.231 9.457 0.843 20.611 9.933 0.813 35.672 7.889 0.841 38.092 8.660

NC

0.821

20.095

0.938

20.079

0.978

20.064

0.968

24

Moment Log

Second

3.0 £ 10 7.002 1.012 1.8 £ 1025 7.164 28.653 7.4 £ 1025 3.467 1.342 1.2 £ 1025 4.215 0.644

2 0.065 2 3.375 2 1.708 2 0.020 212.750 2 1.181 0.259 2.624 2.291 0.031 1.272 0.651

0.252 3.150 1.793 0.091 7.193 0.982 0.048 0.426 0.208 0.407 3.400 2.316

0.804 36.314 4.206 0.965 190.872 20.294 0.676 9.588 7.131 0.827 30.825 8.664

0.061

NC

0.866

0.025

NC

0.991

0.283

2 0.175

0.959

0.234

2 0.156

1.062

3.1 £ 1024 1.645 1.522 3.1 £ 1026 8.236 7.387 3.5 £ 1026 0.484 0.307 3.5 £ 1026 1.091 0.509

2 0.067 2 4.859 2 6.399 2 0.039 217.456 2 1.195 0.006 0.721 0.343 0.010 0.728 0.655

0.027 0.595 2.189 0.084 27.064 0.912 0.445 6.201 2.755 0.244 4.701 2.934

0.664 2.909 2.589 0.999 135.904 37.919 0.871 75.239 22.806 0.892 39.578 29.113

2 0.054

NC

0.610

0.003

2 0.053

1.002

0.229

2 0.131

1.100

0.132

2 0.104

1.024

S. Hoti and M. McAleer

Financial

24

v

Pakistan

0.338 4.850 1.971 0.106 3.542 2.275 20.013 2 11.361 20.722 0.044 1.776 1.132

0.626 11.281 5.840 0.844 22.198 12.800 1.017 12.077 8.130 0.866 10.366 6.633

2 0.181

0.965

2 0.081

0.950

0.005

1.004

2 0.099

0.910

3.2 £ 1024 5.428 1.020 Financial 5.5 £ 1024 6.443 1.701 Political 1.1 £ 1024 4.509 126.839 Composite 6.6 £ 1024 1.670 1.758

0.217 5.159 0.955 0.183 3.002 1.407 20.041 2 33.729 21.848 0.054 1.044 0.605

0.636 13.234 2.275 0.184 1.688 0.484 0.943 47.924 33.644 2 0.303 2 0.429 2 0.438

2 0.264

0.853

2 1.399

0.367

NC

0.902

NC

20.249

Economic

6.7 £ 1025 3.759 1.872 3.2 £ 1025 4.397 1.314 7.5 £ 1024 1.504 2.446 1.1 £ 1024 11.988 10.202

0.245 2.419 1.814 20.013 21.732 20.814 20.038 21.567 24.569 0.189 3.549 1.455

0.175 1.552 0.520 0.238 4.030 2.343 20.032 22.648 25.596 20.235 24.119 21.727

0.616 10.150 5.709 0.883 31.558 18.027 0.320 0.676 1.011 0.597 24.167 6.864

3.5 £ 1024 5.493 0.979 7.4 £ 1024 18.567 4.788 9.3 £ 1025 7.075 2.532 6.5 £ 1024 1.481 1.679

0.308 4.699 0.894 20.054 25.993 23.961 20.039 218.147 22.841 0.066 1.062 0.499

20.195 22.614 20.588 0.392 2.255 1.664 20.004 20.469 20.158 20.042 20.440 20.326

0.622 12.207 2.062 0.058 1.536 1.202 0.952 72.440 28.231 20.286 20.356 20.404

0.333

20.249

0.949

0.106

20.14

0.989

NC

0.267

0.072

20.326

0.669

0.211

NC

0.832

0.143

NC

0.200

20.041

NC

0.911

0.045

NC

20.241

20.054

Notes: (1) NC denotes that the log-moment could not be calculated because [(a þ gI(ht))h2t þ b] in (7) or (ah2t þ b) in (8) was negative for one observation. (2) The three entries for each parameter are their respective estimate, the asymptotic t-ratio and the Bollerslev and Wooldridge (1992) robust t-ratio.

Univariate and Multivariate Estimates

Sri Lanka

6.0 £ 1025 3.880 1.728 Financial 5.0 £ 1025 3.004 1.374 Political 27.1 £ 1026 28.565 26.789 Composite 2.9 £ 1025 1.345 0.810

Economic

379

380

Table 6.2. Univariate GARCH(1,1) and GJR(1,1) estimates for East Asia and the Paciﬁc by risk return Country

Risk Returns

GARCH(1,1)

v

Australia

Economic

Political

Composite

Brunei

Economic

Financial

Political

Composite

b

Moment Log

Second

8.7 £ 1026 1.231 0.435 1.7 £ 1024 2.390 0.882 1.0 £ 1023 9.203 13.590 4.3 £ 1025 2.591 3.156

0.029 1.850 1.238 0.131 1.399 1.084 0.450 4.003 2.391 0.299 3.178 1.925

0.952 31.255 12.262 0.432 1.811 1.250 2 0.143 2 7.385 2 2.917 0.130 0.487 0.857

2 0.024

0.981

2 0.709

0.563

NC

0.307

2 1.253

0.429

3.7 £ 1024 9.933 51.084 9.2 £ 1026 5.123 0.990 1.4 £ 1027 0.066 0.056 1.9 £ 1026 3.823 0.480

0.282 2.354 1.552 0.209 2.595 2.270 2 0.002 2 1.757 2 0.088 0.055 3.454 0.910

2 0.118 2 1.517 2 0.972 0.633 9.018 3.083 0.999 20.421 19.513 0.903 39.727 6.392

NC

0.163

2 0.347

0.842

2 0.003

0.997

2 0.058

0.957

v

a

g

b

a þ g/2

Moment Log

2 1.9 £ 1026 2 0.565 2 0.126 2.5 £ 1024 1.661 3.133 6.5 £ 1025 3.864 2.653 4.0 £ 1025 2.250 2.959 1.8 £ 1024 17.987 48.760 9.1 £ 1026 4.174 1.010 1.0 £ 1026 3.311 1.123 1.6 £ 1025 2.511 11.250

0.050 2.362 2.081 2 0.217 2 6.509 2 1.959 2 0.043 2 0.747 2 4.064 0.224 2.180 1.118

2 0.068 2 2.559 2 0.853 0.385 1.838 2.419 0.524 2.531 1.672 0.178 0.789 0.759

0.994 59.522 14.330 0.489 1.565 2.583 0.388 3.878 1.876 0.170 0.569 1.155

2 0.070 2 12.016 2 2.727 0.267 1.923 1.817 2 0.026 2 13.856 2 0.659 2 0.089 2 7.078 2 3.427

0.290 3.637 2.117 2 0.128 2 1.022 2 0.489 0.030 9.882 0.690 0.171 3.722 1.604

0.439 9.411 6.208 0.633 7.204 3.346 0.990 131.841 24.218 0.673 4.999 5.839

Second

0.016

0.008

1.010

2 0.025

2 0.763

0.465

0.219

2 0.779

0.607

0.313

2 1.080

0.483

0.075

NC

0.514

0.202

2 0.330

0.835

2 0.011

NC

0.979

2 0.004

NC

0.669

S. Hoti and M. McAleer

Financial

a

GJR(1,1)

China

Economic

Financial

Political

Hong Kong

Economic

Financial

Political

Composite

Indonesia

Economic

Financial

2 0.004 2 1.011 2 0.126 0.077 2.091 1.083 0.058 1.537 0.694 2 0.027 2 56.011 2 0.574

0.995 184.486 19.340 0.781 8.152 4.838 0.647 2.593 1.516 1.029 35.622 18.712

2 0.010

0.991

2 0.188

0.858

2 0.366

0.705

2 0.004

1.002

3.9 £ 1024 5.978 0.703 1.3 £ 1025 2.823 0.644 5.6 £ 1025 1.727 1.369 1.2 £ 1025 3.382 1.474

0.105 3.265 0.595 0.059 2.475 1.251 2 0.032 2 2.384 2 3.202 0.327 5.634 2.310

0.686 14.513 1.807 0.787 11.066 3.246 0.846 8.591 5.563 0.713 21.738 7.031

2 0.300

0.791

2 0.185

0.847

NC

0.813

2 0.106

1.040

4.3 £ 1025 6.154 0.734 4.4 £ 1024 10.264 1.209

0.186 6.524 1.522 0.514 5.943 1.353

0.848 54.451 17.463 0.446 8.539 1.823

2 0.051

1.034

2 0.504

0.960

9.9 £ 1027 1.015 0.244 8.7 £ 1025 2.082 1.222 3.5 £ 1025 1.363 0.699 1.5 £ 1025 1.575 1.240

2 0.026 2 3.995 2 0.401 0.126 1.687 1.238 0.050 2.190 0.686 0.050 2.393 0.650

0.040 4.218 0.637 2 0.142 2 1.821 2 1.141 2 0.064 2 2.420 2 0.557 2 0.071 2 2.789 2 0.916

0.999 17.741 14.378 0.759 6.623 7.309 0.872 9.549 4.935 0.906 16.643 10.684

2 0.006

2 0.010

0.993

0.056

2 0.238

0.814

0.018

2 0.121

0.890

0.014

2 0.089

0.921

7.5 £ 1024 5.068 0.734 1.3 £ 1025 2.547 0.669 1.0 £ 1024 5.538 1.639 4.0 £ 1026 3.414 1.699

2 0.054 2 5.320 2 0.614 0.042 2.460 0.545 2 0.044 2 1.846 2 3.273 2 0.041 2 13.706 2 2.432

0.303 3.648 1.109 0.037 1.081 0.318 2 0.013 2 0.346 2 0.754 0.126 7.135 1.615

0.695 12.406 1.616 0.790 9.978 3.458 0.788 19.110 4.592 0.961 99.271 43.156

0.097

NC

0.792

0.061

2 0.184

0.851

NC

0.738

0.022

2 0.036

0.982

2.5 £ 1025 8.922 1.509 4.2 £ 1024 8.778 1.280

2 0.066 2 8.751 2 1.639 0.588 4.997 1.221

0.271 9.700 2.439 2 0.254 2 1.674 2 0.612

0.940 93.341 16.949 0.475 8.136 2.212

0.069

NC

1.009

0.461

NC

0.935

2 0.050

381

(continued)

Univariate and Multivariate Estimates

Composite

8.4 £ 1027 0.751 0.214 7.6 £ 1025 2.191 0.787 9.4 £ 1025 1.357 0.733 2 9.3 £ 1028 2 0.176 2 0.164

382

Table 6.2. Continued Country

Risk Returns

GARCH(1,1)

v

a

b

GJR(1,1) Moment Log

Political

Composite

Economic

Financial

Political

Composite

Malaysia

Economic

Financial

a

g

b

a þ g/2

Second

2.6 £ 10 3.692 1.929 5.6 £ 1026 1.773 1.071

0.375 5.322 2.663 0.212 6.524 1.969

0.694 15.617 10.448 0.832 42.111 11.220

2 0.100

1.068

2 0.027

1.043

1.3 £ 1024 0.684 53.036 7.2 £ 1025 14.685 2.189 1.7 £ 1024 12.939 527.183 6.7 £ 1026 1.653 1.105

2 0.023 2 0.732 2 0.483 0.909 4.497 2.256 0.225 64.444 1.726 0.089 2.047 1.319

0.584 0.943 4.558 0.057 1.051 0.498 2 0.174 2 3.336 2 1.918 0.808 7.884 7.026

NC

0.561

2 2.124

0.966

NC

0.052

2 0.130

0.897

5.3 £ 1025 4.069 0.771 3.8 £ 1024 1.479 22.617

0.084 2.287 1.869 2 0.013 2 29.963 2 0.104

0.823 18.063 5.101 0.573 1.986 2.538

2 0.129

0.907

NC

0.560

25

Moment Log

Second

2.7 £ 10 3.734 2.105 6.0 £ 1026 2.658 1.054

0.203 4.192 1.673 0.054 1.926 1.135

0.388 3.351 1.406 0.203 4.176 1.516

0.688 14.153 12.006 0.875 38.822 14.825

0.397

NC

1.085

0.155

NC

1.030

1.9 £ 1024 1.548 1.688 1.7 £ 1026 11.533 1.377 2.7 £ 1025 3.008 1.478 9.5 £ 1026 2.911 0.674

2 0.098 2 2.977 2 4.114 2 0.025 2 13.472 2 0.462 2 0.009 2 0.598 2 0.217 2 0.045 2 1.527 2 1.456

0.079 1.287 4.469 0.100 14.102 1.881 0.517 3.677 1.458 0.187 2.201 1.007

0.469 1.308 1.168 0.971 42.095 21.201 0.684 9.735 3.772 0.800 11.077 2.660

2 0.058

NC

0.411

0.025

2 0.034

0.996

0.250

2 0.221

0.933

0.048

2 0.197

0.848

1.7 £ 1024 2.786 165.466 3.9 £ 1024 1.443 24.870

0.003 0.138 0.071 0.009 0.069 0.059

2 0.027 2 1.258 2 0.576 2 0.022 2 0.168 2 0.279

0.687 5.462 7.435 0.577 1.981 2.652

2 0.011

NC

0.676

2 0.002

NC

0.575

S. Hoti and M. McAleer

Japan

25

v

Political

Composite

Mongolia

Economic

Political

Composite

New Zealand

Economic

Financial

Political

Composite

0.027 0.537 0.487 0.504 4.442 1.369

0.320 0.225 0.240 2 0.107 2 1.702 2 1.726

2 1.069

0.347

NC

0.397

1.2 £ 1023 11.308 1.890 2.7 £ 1025 7.279 1.332 3.5 £ 1024 10.905 4.398 1.0 £ 1024 11.037 2.124

1.097 4.690 1.405 0.163 5.493 0.831 0.546 4.317 1.115 0.805 5.414 2.134

0.226 4.719 1.125 0.838 38.685 9.158 2 0.035 2 0.634 2 1.199 0.257 5.858 1.580

2 1.070

1.323

2 0.098

1.001

NC

0.511

2 0.857

1.062

1.9 £ 1024 3.475 2.123 3.7 £ 1024 1.446 0.878 3.1 £ 1025 2.626 1.143 7.8 £ 1026 3.030 1.926

0.018 0.525 0.277 0.101 1.006 1.281 0.130 2.215 1.500 0.594 5.309 1.808

0.481 3.227 1.950 0.463 1.255 1.544 0.535 3.251 1.642 0.481 10.393 2.905

2 0.696

0.499

2 0.683

0.564

2 0.477

0.666

2 0.223

1.075

1.8 £ 1024 4.047 3.852 1.9 £ 1024 2.092 3.368

2 0.049 2 1.487 2 3.393 0.177 1.210 0.547

0.263 2.080 1.557 2 0.190 2 1.447 2 0.583

2 0.202 2 0.779 2 1.135 2 0.242 2 0.446 2 0.612

0.083

NC

2 0.119

0.082

NC

2 0.160

1.2 £ 1023 9.693 1.850 1.1 £ 1024 4.483 1.920 1.7 £ 1024 4.811 101.682 1.0 £ 1024 10.580 2.298

0.973 4.102 0.874 2 0.021 2 3.790 2 0.878 2 0.030 2 1.688 2 1.311 0.572 4.556 1.173

0.085 0.144 0.083 0.354 4.024 0.823 0.627 3.293 1.431 0.942 1.943 1.338

0.221 3.230 1.056 0.694 10.499 5.551 0.559 8.756 7.191 0.210 4.581 1.485

1.016

2 1.107

1.237

0.156

NC

0.850

0.284

NC

0.843

1.043

2 1.090

1.253

2.1 £ 1024 4.140 3.099 8.9 £ 1026 18.133 17.285 4.3 £ 1025 14.184 13.227 6.7 £ 1024 2.699 1.880

2 0.067 2 5.207 2 5.815 2 0.114 2 20.783 2 1.225 2 0.107 2 7.316 2 2.815 0.840 4.820 1.516

0.181 2.169 1.338 0.142 32.101 1.965 0.510 4.758 2.419 2 0.411 2 1.978 2 0.801

0.435 3.117 2.145 0.995 43.787 19.013 0.392 71.004 3.697 0.503 9.899 3.639

0.024

NC

0.459

2 0.043

NC

0.952

0.148

NC

0.540

0.635

2 0.182

1.138

383

(continued)

Univariate and Multivariate Estimates

Financial

1.1 £ 1024 0.470 0.481 1.1 £ 1024 9.847 4.018

384

Table 6.2. Continued Country

Risk Returns

GARCH(1,1)

v

North Korea

Economic

Financial

Composite

Papua New Guinea

Economic

Financial

Political

Composite

b

5.4 £ 1023 1.609 1.609 7.2 £ 1024 1.160 1.697 5.7 £ 1026 11.398 7.343 1.3 £ 1025 6.140 15.344

2 0.011 2 1.599 2 1.599 2 0.028 2 1.606 2 3.084 2 0.028 2 31.019 2 0.921 2 0.022 2 30.031 2 0.710

0.590 2.306 2.306 0.565 1.478 1.581 1.019 273.128 26.376 1.002 207.787 26.461

1.1 £ 1025 1.107 9.936 4.0 £ 1024 7.799 2.403 6.3 £ 1025 1.834 0.332 4.5 £ 1025 1.260 0.749

2 0.014 2 17.205 2 0.309 0.314 4.060 1.517 0.034 1.564 0.538 0.079 1.338 1.293

1.018 106.154 15.572 0.122 1.256 0.628 0.747 5.487 1.116 0.748 3.882 3.343

Moment Log

Second

NC

0.579

NC

0.537

2 0.029

0.991

2 0.034

0.980

2 0.003

1.004

2 1.646

0.437

2 0.257

0.781

2 0.214

0.827

v

a

g

b

a þ g/2

Moment Log

Second

2.9 £ 1023 17.527 1.609 1.5 £ 1024 3.015 11.301 1.8 £ 1024 1.265 6.307 3.8 £ 1025 5.797 34.566

2 0.005 2 0.668 2 0.383 2 0.042 2 7.154 2 0.945 2 0.091 2 5.917 2 2.174 2 0.017 2 10.319 2 1.589

2 0.091 2 2.010 2 2.149 0.032 24.910 0.768 0.071 1.994 1.990 2 0.019 2 5.950 2 0.616

0.797 59.784 8.303 0.927 31.297 28.522 0.503 1.232 3.125 0.959 74.792 23.891

2 0.051

NC

0.746

2 0.026

NC

0.901

2 0.056

NC

0.447

2 0.026

NC

0.933

4.4 £ 1024 2.963 1.524 5.5 £ 1024 21.363 27.647 4.9 £ 1025 2.917 1.340 5.4 £ 1025 2.345 1.306

2 0.127 2 10.178 2 2.605 0.533 3.331 1.441 2 0.036 2 2.338 2 1.565 2 0.080 2 18.296 2 1.779

0.120 5.817 2.984 2 0.480 2 2.765 2 1.238 0.166 2.655 1.520 0.158 3.988 1.607

0.830 13.414 4.000 2 0.081 2 3.477 2 1.553 0.781 10.468 5.749 0.836 10.604 6.479

2 0.067

NC

0.763

0.293

NC

0.211

0.047

NC

0.828

2 0.001

NC

0.835

S. Hoti and M. McAleer

Political

a

GJR(1,1)

Philippines

Economic

Financial

Political

Singapore

Economic

Financial

Political

Composite

South Korea

Economic

Financial

0.040 1.152 0.574 0.086 3.246 1.416 2 0.023 2 0.548 2 4.080 2 0.015 2 1.487 2 1.898

0.611 1.992 0.700 0.773 16.079 8.221 0.568 0.760 2.050 0.925 24.204 17.799

2 0.440

0.650

2 0.178

0.859

NC

0.545

2 0.098

0.910

2.2 £ 1024 7.465 2.253 1.5 £ 1025 1.460 0.367 7.2 £ 1026 5.229 7.068 3.3 £ 1025 2.377 1.894

0.495 3.106 2.305 0.029 1.594 0.660 2 0.040 2 5.194 2 1.359 0.177 1.756 2.005

0.273 3.131 1.690 0.813 6.453 1.933 0.956 97.503 21.003 0.393 1.692 1.561

2 0.804

0.768

2 0.179

0.842

2 0.098

0.916

2 0.683

0.570

1.0 £ 1024 4.080 0.920 1.8 £ 1024 2.432 15.888

0.379 3.105 1.821 2 0.015 2 0.645 2 0.137

0.547 5.117 4.385 0.600 3.695 3.250

2 0.411

0.926

NC

0.586

7.1 £ 1025 1.323 0.512 1.5 £ 1024 4.185 1.357 1.7 £ 1024 3.360 1.871 8.3 £ 1025 2.129 1.751

2 0.018 2 1.005 2 0.475 0.085 2.609 1.044 2 0.025 2 7.697 2 3.764 0.131 1.819 1.266

0.066 1.873 0.827 0.001 0.023 0.008 2 0.076 2 4.172 2 2.135 2 0.174 2 2.264 2 1.646

0.886 10.671 4.263 0.774 15.596 8.327 0.894 21.120 11.564 0.753 6.740 5.952

2.3 £ 1024 8.237 2.981 3.4 £ 1025 3.041 23.803 8.7 £ 1026 4.135 12.092 3.4 £ 1025 4.514 2.435

2 0.040 2 1.619 2 4.080 2 0.051 2 5.056 2 1.038 2 0.041 2 5.842 2 1.612 2 0.005 2 0.124 2 0.118

1.202 3.456 2.228 0.094 2.931 1.303 2 0.035 2 2.680 2 2.393 0.621 2.402 2.504

0.289 3.576 2.865 0.744 8.433 9.495 0.951 47.817 22.764 0.321 2.246 1.760

1.2 £ 1024 4.954 0.997 1.6 £ 1024 1.005 4.764

0.263 2.010 1.571 2 0.014 2 15.975 2 0.048

0.360 2.012 0.604 0.161 0.500 0.340

0.493 5.030 3.828 0.613 1.685 2.453

0.015

2 0.107

0.902

0.086

2 0.178

0.860

NC

0.830

0.044

2 0.252

0.797

0.561

NC

0.851

2 0.005

NC

0.739

2 0.059

NC

0.893

0.305

2 0.844

0.626

0.443

2 0.492

0.936

0.066

NC

0.679

2 0.063

385

(continued)

Univariate and Multivariate Estimates

Composite

2.5 £ 1024 1.231 0.412 1.5 £ 1024 4.288 1.351 4.9 £ 1024 0.593 1.790 3.6 £ 1025 2.211 1.257

386

Table 6.2. Continued Country

Risk Returns

GARCH(1,1)

v

Political

Taiwan

Economic

Financial

Political

Composite

Thailand

Economic

b

Moment Log

Second

1.7 £ 1025 2.171 0.719 1.7 £ 1025 2.075 0.716

0.075 2.043 1.303 0.253 5.544 1.262

0.820 10.358 5.242 0.809 26.001 12.393

2 0.136

0.895

2 0.045

1.062

6.1 £ 1025 7.604 1.770 1.5 £ 1025 1.823 0.417 2.4 £ 1025 9.473 18.726 2.0 £ 1025 1.098 1.602

0.629 7.576 1.719 0.047 1.233 2.071 2 0.026 2 12.429 2 0.938 2 0.029 2 22.137 2 0.781

0.512 13.609 3.134 0.858 11.305 3.672 0.859 54.373 12.993 0.731 2.735 4.824

2 0.335

1.140

2 0.121

0.905

NC

0.833

2 0.046

0.701

2.0 £ 1025 2.770 1.204

0.111 4.416 1.671

0.872 32.514 14.067

2 0.047

0.983

v

a

g

b

a þ g/2

Moment Log

Second

1.9 £ 1025 3.568 4.520 2.2 £ 1025 2.635 1.074

2 0.025 2 2.599 2 0.236 0.090 2.728 0.649

0.192 2.356 1.707 0.238 5.064 0.601

0.803 13.816 6.145 0.824 32.570 10.003

0.071

NC

0.874

0.209

2 0.059

1.033

6.4 £ 1025 7.118 1.899 6.5 £ 1025 3.985 1.200 5.6 £ 1025 1.848 43.065 1.1 £ 1026 1.617 0.507

0.496 7.074 0.719 0.864 1.930 1.185 2 0.028 2 9.455 2 0.926 2 0.011 2 1.340 2 0.250

0.655 1.646 0.712 2 0.659 2 1.463 2 0.809 0.200 2.265 0.674 0.114 3.497 1.143

0.457 8.590 2.868 0.327 1.965 2.215 0.550 2.285 5.128 0.947 43.707 13.031

0.824

2 0.389

1.281

0.534

2 0.878

0.861

0.072

NC

0.622

0.046

2 0.021

0.993

3.2 £ 1025 4.353 12.889

2 0.042 2 6.337 2 0.427

0.220 4.626 1.355

0.893 39.531 19.598

0.068

NC

0.961

S. Hoti and M. McAleer

Composite

a

GJR(1,1)

Financial

Political

Composite

Economic

Financial

Political

Composite

0.083 0.949 1.055 0.228 5.921 1.235 1.094 8.533 2.019

0.697 2.981 3.230 2 0.206 2 17.345 2 1.174 0.009 0.212 0.856

2 0.316

0.780

NC

0.022

2 1.902

1.102

1.2 £ 1023 2.112 0.695 2.4 £ 1024 2.066 1.466 1.4 £ 1024 0.414 15.955 2.0 £ 1025 8.438 46.398

0.061 0.818 1.091 2 0.026 2 6.116 2 2.665 2 0.015 2 0.626 2 0.156 2 0.042 2 25.054 2 8.258

0.132 0.322 0.124 0.872 12.653 6.845 0.607 0.631 3.004 0.977 15.241 12.119

2 1.855

0.193

NC

0.846

NC

0.591

NC

0.935

5.6 £ 1025 69.610 2.468 1.9 £ 1024 5.053 2.142 8.1 £ 1025 9.319 4.364

2 0.164 2 5.386 2 2.247 2 0.038 2 2.410 2 4.215 1.215 6.352 1.267

0.215 5.297 2.842 0.471 3.999 1.050 2 0.262 2 0.634 2 0.254

0.976 87.603 20.416 0.314 2.542 1.106 0.014 0.323 0.979

2 0.056

1.4 £ 1024 20.668 2.000 7.0 £ 1024 0.396 1.372 2.1 £ 1024 0.754 1.492 1.4 £ 1024 0.555 1.192

0.016 1.764 0.530 0.008 0.147 0.188 0.032 1.296 0.632 0.009 0.274 0.136

2 0.113 2 5.660 2 2.561 2 0.032 2 0.356 2 0.817 2 0.168 2 0.865 2 3.968 2 0.055 2 0.499 2 0.891

0.919 18.826 11.024 0.573 0.530 1.540 0.540 0.891 1.239 0.535 0.635 1.134

2 0.081

0.921

0.198

NC

0.512

1.084

2 1.783

1.098

2 0.040

2 0.068

0.879

2 0.008

2 0.547

0.566

2 0.052

2 0.588

0.488

2 0.019

2 0.612

0.516

Notes: (1) NC denotes that the log-moment could not be calculated because [(a þ gI(ht))h2t þ b] in (7) or (ah2t þ b) in (8) was negative for one observation. (2) The three entries for each parameter are their respective estimate, the asymptotic t-ratio and the Bollerslev and Wooldridge (1992) robust t-ratio.

Univariate and Multivariate Estimates

Vietnam

2.7 £ 1024 1.296 0.657 4.2 £ 1024 13.557 3.954 8.2 £ 1025 9.331 4.379

387

388

Table 6.3. Univariate GARCH(1,1) and GJR(1,1) estimates for East Europe by risk return Country

Risk Returns

GARCH(1,1)

v

a

b

GJR(1,1) Moment Log

Albania

Economic

Political Composite

Bulgaria

Economic Financial Political Composite

a

g

b

a þ g/2

Second

2.7 £ 10 9.596 0.763 6.6 £ 1025 9.659 0.956 1.8 £ 1025 7.168 1.228 2.5 £ 1025 5.768 1.411

0.218 5.498 1.073 0.263 6.912 1.036 0.513 6.640 1.996 0.599 5.210 2.690

0.819 63.915 6.692 0.735 41.845 4.316 0.665 22.768 7.044 0.580 14.971 6.100

20.090

1.038

20.160

0.997

20.098

1.177

20.160

1.180

2.6 £ 1023 1.086 0.834 4.2 £ 1025 3.791 1.117 4.4 £ 1024 7.823 1.496 2.6 £ 1025 2.394 0.780

20.025 215.117 27.050 0.371 3.238 1.852 0.218 2.751 1.700 0.079 2.738 1.407

0.584 1.446 1.108 0.760 19.295 23.853 20.202 22.135 21.982 0.874 19.778 9.168

NC

0.558

20.087

1.132

NC

0.016

20.066

0.952

24

Moment Log

Second

2.40 £ 10 7.950 1.060 7.28 £ 1025 8.956 1.882 1.80 £ 1025 6.072 1.526 2.47 £ 1025 5.721 1.459

0.081 2.719 0.603 20.064 26.538 21.597 0.166 2.602 1.094 0.394 3.724 2.262

0.363 5.097 1.288 0.378 131.343 2.204 0.578 5.139 1.407 0.321 2.083 0.802

0.820 53.914 9.878 0.842 48.542 13.198 0.689 21.703 8.822 0.606 15.052 7.060

0.262

20.139

1.083

0.125

NC

0.966

0.455

20.224

1.144

0.555

20.208

1.160

2.52 £ 1023 1.539 2.240 4.58 £ 1025 7.325 2.263 3.86 £ 1024 8.157 3.016 1.73 £ 1025 4.726 2.574

20.025 20.916 21.946 20.008 20.348 20.168 0.197 2.968 0.871 20.055 24.731 22.456

0.131 0.833 1.070 0.721 7.241 1.554 20.053 20.429 20.211 0.289 4.271 3.112

0.574 2.230 3.094 0.790 35.174 21.549 20.195 21.954 20.820 0.908 42.255 27.627

0.041

NC

0.615

0.353

20.247

1.143

0.170

NC

0.090

20.177

20.025 0.998

S. Hoti and M. McAleer

Financial

24

v

Czech Republic

Economic Financial Political

Hungary

Economic Financial Political Composite

Poland

Economic Financial

20.029 20.358 22.517 20.019 20.096 21.819 20.065 21.649 23.521 0.990 5.791 1.750

0.559 0.437 1.093 0.564 0.217 0.997 0.282 0.415 0.589 0.049 0.583 0.886

5.7 £ 1025 3.268 1.063 5.4 £ 1024 7.718 2.049 3.9 £ 1025 1.536 43.424 1.2 £ 1024 5.785 5.784

0.135 4.948 1.462 0.203 3.369 0.951 20.046 22.399 21.283 0.358 4.031 1.877

3.5 £ 1025 4.228 0.727 5.6 £ 1024 1.449 0.612

0.125 4.707 1.757 20.026 21.395 21.137

NC

0.530

NC

0.544

NC

0.218

21.149

1.039

0.826 33.339 8.229 20.004 20.036 20.041 0.765 4.485 9.933 20.077 20.723 21.747

20.075

0.961

NC

0.199

NC

0.719

NC

0.281

0.850 41.684 8.898 0.196 0.341 0.149

20.060

0.975

NC

0.170

3.42 £ 1024 1.903 0.773 7.58 £ 1024 0.211 2.490 8.94 £ 1025 1.679 1.394 4.72 £ 1025 3.398 4.083

20.125 23.961 23.452 0.002 0.011 0.003 20.066 22.176 22.589 1.469 4.532 1.396

0.090 7.080 3.653 20.023 21.100 20.033 0.158 1.857 1.838 21.104 22.544 21.062

0.537 2.114 0.751 0.569 0.278 1.348 0.600 2.552 1.688 0.065 0.736 0.996

20.080

NC

0.457

20.009

20.562

0.559

0.013

NC

0.613

0.917

20.737

0.982

6.75 £ 1025 3.264 1.251 6.24 £ 1024 16.179 2.912 1.80 £ 1025 4.841 11.962 1.29 £ 1024 4.660 5.778

0.184 3.514 1.729 20.037 21.299 22.273 20.058 24.518 21.213 0.446 3.184 1.768

20.088 21.531 20.654 0.493 2.653 1.086 0.052 1.938 1.165 20.178 21.053 20.802

0.813 29.499 8.004 0.038 0.740 0.309 0.933 76.799 22.424 20.112 20.675 21.880

0.140

20.073

0.953

0.210

NC

0.248

20.032

NC

0.901

0.357

NC

0.245

3.42 £ 1025 4.201 0.736 6.49 £ 1025 3.420 1.036

0.170 3.514 1.780 20.024 22.314 21.163

20.091 21.451 20.955 0.095 2.997 1.251

0.850 40.639 9.507 0.882 23.469 8.993

0.124

20.031

0.975

0.024

20.161

0.906

389

(continued)

Univariate and Multivariate Estimates

Composite

2.5 £ 1024 0.349 1.150 7.4 £ 1024 0.168 1.245 1.7 £ 1024 1.138 1.563 4.6 £ 1025 3.488 3.863

Country

Risk Returns

GARCH(1,1)

v

a

b

GJR(1,1) Moment Log

Political Composite

Economic Financial Political Composite

Russia

Economic Financial Political

24

1.7 £ 10 2.848 2.839 6.4 £ 1025 1.261 0.831

20.082 25.411 25.151 20.026 21.502 21.058

0.400 1.565 1.604 0.733 3.301 2.173

8.7 £ 1024 1.520 1.039 5.0 £ 1024 3.018 0.332 1.4 £ 1024 2.594 0.959 3.2 £ 1024 3.946 2.264

0.087 1.520 1.062 0.034 2.683 0.569 0.102 2.473 0.946 0.185 4.087 0.713

4.9 £ 1023 0.295 0.486 4.8 £ 1025 3.531 1.625 6.4 £ 1024 0.904

20.025 20.214 22.427 0.232 3.760 0.792 20.035 21.068

v

a

g

b

a þ g/2

Second

NC

0.317

20.355

0.707

0.669 3.197 3.052 0.860 18.890 2.750 0.524 3.188 1.220 0.060 0.328 0.167

20.322

0.756

20.128

0.893

20.524

0.627

22.073

0.246

0.418 0.212 0.319 0.830 41.165 6.228 0.530 0.963

NC

0.393

20.054

1.061

NC

0.496

24

1.32 £ 10 2.214 1.446 2.17 £ 1024 5.721 1.814

20.069 217.657 24.893 0.094 0.865 0.893

0.218 2.033 1.452 20.204 21.751 21.941

0.436 1.718 1.079 0.333 2.839 0.876

4.30 £ 1024 4.597 2.608 6.78 £ 1025 10.224 1.333 1.30 £ 1025 2.066 1.461 2.81 £ 1024 4.454 1.855

20.078 261.658 23.331 20.092 247.638 20.734 0.139 1.856 1.213 0.375 4.274 0.815

0.156 6.323 3.288 0.146 37.644 0.997 20.062 20.680 20.249 20.362 23.323 20.797

0.905 31.746 19.061 0.996 570.112 45.223 0.532 2.650 1.971 0.159 1.112 0.394

3.78 £ 1023 3.004 1.052 9.54 £ 1024 7.233 2.232 4.85 £ 1024 0.960

20.118 22.649 22.556 0.074 0.700 0.532 20.038 20.955

0.095 2.679 2.745 0.883 2.300 0.882 0.004 0.105

0.530 3.359 0.886 0.082 0.766 0.675 0.650 1.683

Moment Log

Second

NC

0.477

20.967

0.324

0.000

NC

0.905

20.018

NC

0.978

0.109

20.481

0.641

0.194

21.183

0.353

NC

0.459

22.206

0.597

NC

0.614

0.041 20.008

20.071 0.516 20.036

S. Hoti and M. McAleer

Romania

390

Table 6.3. Continued

Composite

Slovakia

Economic Financial

Composite

Yugoslavia

Economic Financial Political Composite

22.712 0.299 3.484 0.847

1.004 0.122 0.415 0.695

1.4 £ 1024 3.805 0.631 3.9 £ 1025 0.982 0.852 5.3 £ 1025 2.735 13.751 1.6 £ 1024 4.387 2.497

0.043 1.232 0.807 0.068 1.202 1.198 20.137 23.193 22.922 0.293 2.832 1.525

0.877 20.938 5.081 0.852 6.561 6.898 0.875 14.469 18.722 20.102 20.466 21.278

2.5 £ 1024 6.110 1.932 2.5 £ 1025 1.741 0.209 5.5 £ 1023 7.733 3.903 5.5 £ 1024 4.562 2.597

0.339 4.693 2.200 0.091 7.524 1.642 0.433 36.104 1.290 0.444 3.102 1.990

0.643 15.460 8.346 0.946 113.017 21.577 20.076 20.624 21.483 0.289 2.346 2.019

21.479

0.420

NC

0.920

20.103

0.919

0.034

0.737

NC

0.191

20.217

0.983

0.001

1.037

NC

0.357

20.786

0.733

1.090 5.55 £ 1024 2.281 2.177

20.759 0.200 1.367 0.556

0.086 0.160 0.649 0.290

1.648 0.111 0.328 0.688

1.17 £ 1023 1.629 1.622 4.08 £ 1025 2.561 6.194 6.67 £ 1025 6.487 3.866 2.75 £ 1024 1.923 3.402

20.235 22.172 21.678 20.126 28.056 21.288 20.164 25.287 211.348 20.046 20.384 20.717

0.433 2.134 1.418 0.222 4.290 2.809 0.027 0.420 0.805 0.470 1.107 1.302

0.467 1.236 1.620 0.936 19.881 13.274 0.827 22.765 12.623 0.014 0.026 0.056

3.43 £ 1024 4.247 1.873 2.83 £ 1025 2.060 0.226 2.08 £ 1023 4.406 1.787 4.47 £ 1024 4.425 2.690

0.096 1.803 0.726 0.069 3.212 1.263 20.017 20.818 22.658 0.205 2.502 0.743

0.450 2.823 1.380 0.046 1.571 0.467 0.647 2.726 1.564 0.630 2.155 1.442

0.618 8.280 7.459 0.945 122.200 18.978 0.365 2.586 1.593 0.347 2.743 2.734

0.280

21.667

0.390

20.019

NC

0.448

20.015

NC

0.921

20.150

NC

0.677

0.189

NC

0.203

0.322

20.380

0.939

0.091

20.010

1.037

0.306

NC

0.671

0.520

20.804

0.867

391

Notes: (1) NC denotes that the log-moment could not be calculated because [(a þ gI(ht))h2t þ b] in (7) or (ah2t þ b) in (8) was negative for one observation. (2) The three entries for each parameter are their respective estimate, the asymptotic t-ratio and the Bollerslev and Wooldridge (1992) robust t-ratio.

Univariate and Multivariate Estimates

Political

1.041 5.4 £ 1024 2.578 2.083

392

Table 6.4. Univariate GARCH(1,1) and GJR(1,1) estimates for Middle East and North Africa by risk return Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Algeria

Economic

Financial

Composite

Bahrain

Economic

Financial

Political

Composite

a

g

b

a þ g/2

Second

6.4 £ 10 2.734 0.867 6.5 £ 1024 1.390 0.598 3.9 £ 1024 0.748 0.643 1.5 £ 1024 1.708 1.381

0.099 2.243 1.015 0.053 1.154 0.662 0.036 0.825 0.482 0.138 1.790 1.658

0.521 3.330 1.054 0.333 0.737 0.312 0.297 0.332 0.279 0.442 1.525 1.380

2 0.526

0.619

2 0.988

0.386

2 1.121

0.334

2 0.608

0.581

6.3 £ 1025 3.385 1.755 3.4 £ 1024 0.745 6.723 1.4 £ 1024 17.401 2.938 9.8 £ 1025 16.451 2.901

0.331 3.489 1.634 2 0.008 2 6.982 2 0.031 1.370 6.767 0.587 0.638 6.356 1.010

0.640 9.394 8.055 0.581 1.031 1.749 2 0.020 2 1.664 2 1.136 2 0.030 2 0.847 2 0.957

2 0.199

0.971

NC

0.573

NC

1.351

NC

0.608

23

Moment Log

Second

1.3 £ 10 5.393 3.738 7.2 £ 1024 2.953 1.131 1.3 £ 1024 7.904 58.021 1.4 £ 1024 1.638 1.285

2 0.001 2 0.043 2 0.027 2 0.022 2 0.674 2 0.379 2 0.094 2 6.495 2 6.026 0.150 1.484 1.311

0.530 2.180 1.774 0.180 1.523 1.364 0.377 5.190 2.961 2 0.044 2 0.402 2 0.307

0.046 0.335 0.320 0.252 1.041 0.398 0.732 126.956 19.968 0.481 1.701 1.492

0.264

2 3.108

0.310

0.068

NC

0.319

0.094

NC

0.826

0.129

2 0.525

0.609

8.8 £ 1025 4.036 2.384 1.4 £ 1024 1.701 8.198 1.6 £ 1024 14.760 2.859 2.5 £ 1025 6.062 1.848

2 0.050 2 1.620 2 0.320 0.010 0.328 0.565 0.411 4.453 0.745 0.126 1.997 1.055

0.626 3.793 1.546 2 0.338 2 3.188 2 0.657 2.496 5.775 0.371 1.447 5.403 1.480

0.612 8.453 4.192 0.835 8.917 6.853 2 0.041 2 5.886 2 0.353 0.464 12.114 2.767

0.263

NC

0.875

2 0.174

0.676

1.659

NC

1.618

0.850

2 0.610

1.314

2 0.159

S. Hoti and M. McAleer

Political

24

v

Egypt

Economic

Financial

Political

Iran

Economic

Financial

Political

Composite

Iraq

Economic

Financial

0.746 13.325 0.754 0.387 6.340 2.551 0.248 4.422 1.858 0.718 5.646 2.335

0.575 31.180 2.422 0.774 48.031 18.638 0.741 13.445 9.266 0.258 3.071 2.507

2 0.186

1.321

2 0.065

1.161

2 0.098

0.989

2 0.618

0.976

1.3 £ 1024 3.986 0.909 1.2 £ 1023 15.646 4.132 3.2 £ 1025 3.223 1.238 8.1 £ 1024 6.497 4.301

0.125 4.044 1.389 0.444 10.049 1.927 0.126 5.739 1.298 0.218 1.779 1.367

0.828 25.920 6.724 2 0.093 2 2.535 2 1.760 0.835 48.260 7.922 2 0.217 2 2.187 2 1.337

2 0.088

0.952

NC

0.351

2 0.078

0.961

NC

0.001

2.4 £ 1022 5.319 2.225 2.1 £ 1022 18.026 2.144

0.071 1.430 0.972 0.177 2.592 0.465

2 0.477 2 1.730 2 1.082 2 0.094 2 2.904 2 0.506

NC

2 0.406

NC

0.083

2.4 £ 1024 14.295 14.180 3.6 £ 1024 3.477 2.742 2.3 £ 1025 3.591 2.124 9.0 £ 1025 4.647 2.654

2 0.062 2 2.598 2 1.855 0.270 3.384 1.408 2 0.023 2 0.861 2 0.597 0.555 5.571 1.366

0.780 14.526 0.504 2 0.300 2 4.322 2 1.541 0.396 5.537 2.133 0.335 1.126 0.518

0.663 43.454 3.301 0.606 5.656 4.746 0.812 21.128 17.341 0.260 2.677 2.197

0.328

NC

0.991

0.120

2 0.350

0.726

0.175

2 0.240

0.987

0.723

2 0.710

0.982

1.1 £ 1024 5.025 0.772 1.1 £ 1023 21.805 4.122 6.1 £ 1025 4.129 1.055 4.5 £ 1024 11.332 4.470

2 0.032 2 4.479 2 0.862 0.477 4.255 1.517 2 0.002 2 0.072 2 0.127 0.254 1.865 1.717

0.301 4.960 2.308 2 0.138 2 0.755 2 0.423 0.330 3.971 1.479 0.013 0.073 0.035

0.862 37.589 6.909 2 0.080 2 3.735 2 1.469 0.793 20.901 6.431 2 0.101 2 7.530 2 1.976

0.118

2 0.197

0.980

0.408

NC

0.328

0.163

2 0.234

0.956

0.261

NC

0.160

1.1 £ 1022 1.166 1.013 5.0 £ 1023 2.364 1.646

0.056 2.294 0.226 2 0.014 2 2.381 2 2.259

2 0.083 2 3.044 2 0.341 0.165 2.195 0.827

0.255 0.398 0.346 0.744 6.796 5.641

0.014

2 1.289

0.269

0.069

NC

0.812

393

(continued)

Univariate and Multivariate Estimates

Composite

1.4 £ 1024 6.909 1.399 1.4 £ 1025 6.108 1.471 2.7 £ 1025 2.667 1.778 8.9 £ 1025 5.569 2.704

Risk Returns

GARCH(1,1)

v

Political

Composite

Economic

Financial

Political

Composite

Jordan

Economic

Financial

a

GJR(1,1)

b

Moment Log

Second

2.0 £ 1024 3.419 0.608 4.0 £ 1024 2.253 0.835

0.091 2.285 2.513 2 0.029 2 2.543 2 1.608

0.840 18.578 6.058 0.862 12.723 3.916

2 0.104

0.931

2 0.193

0.833

1.1 £ 1024 4.641 1.192 2.5 £ 1025 6.193 0.957 7.5 £ 1025 3.996 0.811 3.0 £ 1025 5.149 0.639

0.224 3.126 2.094 0.249 6.177 1.790 0.148 4.955 1.534 0.135 4.711 1.199

0.662 10.998 3.906 0.762 30.484 5.892 0.791 25.749 4.946 0.773 27.431 3.264

2 0.214

0.886

2 0.130

1.011

2 0.107

0.939

2 0.135

0.908

4.6 £ 1024 2.175 0.925 7.3 £ 1024 10.042 3.763

0.088 1.708 0.934 0.352 11.515 0.382

0.348 1.222 0.568 2 0.112 2 1.130 2 1.050

2 0.906

0.437

NC

0.240

v

a

g

b

a þ g/2

Moment Log

Second

2 0.106

1.103

NC

0.572

3.4 £ 1024 5.490 1.704 1.1 £ 1023 1.349 1.124

0.958 4.366 2.002 2 0.067 2 2.519 2 4.781

2 0.849 2 3.816 2 1.690 0.045 1.481 3.223

0.570 15.029 5.072 0.616 2.095 1.408

1.0 £ 1024 4.530 1.063 2.6 £ 1025 6.042 1.280 1.6 £ 1024 5.868 0.879 4.9 £ 1025 6.534 0.958

0.201 3.018 1.227 0.293 4.832 1.408 0.260 3.679 1.362 0.170 4.455 1.165

0.099 0.967 0.462 2 0.203 2 3.536 2 0.746 2 0.314 2 3.655 2 1.520 2 0.266 2 6.888 2 1.163

0.665 10.691 3.804 0.769 28.373 8.224 0.723 21.699 2.659 0.790 25.344 4.040

0.250

2 0.225

0.915

0.192

2 0.100

0.961

0.103

2 0.115

0.826

0.037

2 0.092

0.827

1.1 £ 1023 10.544 2.162 6.0 £ 1024 25.200 3.817

0.121 4.636 0.792 0.515 9.315 0.342

2 0.160 2 15.469 2 1.066 2 0.539 2 10.531 2 0.358

2 0.345 2 3.240 2 0.750 2 0.008 2 8.180 2 7.590

0.041

NC

2 0.304

0.246

NC

0.238

0.533

2 0.045

S. Hoti and M. McAleer

Israel

394

Table 6.4. Continued Country

Political

Composite

Kuwait

Economic

Political

Composite

Lebanon

Economic

Financial

Political

Composite

1.373 10.420 1.370 1.161 10.173 1.671

2 0.005 2 0.286 2 0.389 2 0.002 2 0.039 2 0.137

NC

1.369

NC

1.160

5.8 £ 1024 6.787 1.309 4.0 £ 1023 1.468 0.625 4.0 £ 1024 3.088 1.062 3.8 £ 1024 3.840 1.059

0.862 6.421 1.166 0.086 0.874 1.130 0.150 1.708 1.057 0.076 2.990 0.728

0.449 19.616 1.717 0.769 5.075 4.261 0.756 9.552 8.525 0.818 17.726 9.624

2 0.402

1.311

2 0.240

0.855

2 0.223

0.906

2 0.170

0.894

2.9 £ 1024 3.028 1.108 2.9 £ 1024 6.054 1.385 5.0 £ 1025 6.199 0.802 3.2 £ 1025 2.098 0.790

2 0.022 2 4.190 2 2.706 0.254 4.229 1.358 0.106 6.009 1.359 0.068 2.978 1.609

0.936 39.334 13.109 0.722 25.742 5.710 0.876 57.757 10.656 0.900 26.012 17.178

NC

0.914

2 0.176

0.976

2 0.052

0.982

2 0.045

0.968

8.0 £ 1024 7.592 6.337 1.5 £ 1024 10.761 4.791

0.667 7.167 0.401 2.128 13.904 1.610

2 0.723 2 5.272 2 0.434 2 1.857 2 14.700 2 1.402

0.033 0.395 0.344 2 0.019 2 1.299 2 1.437

0.306

2 2.444

0.339

1.200

NC

1.180

9.5 £ 1024 9.480 306.573 1.1 £ 1022 1.479 2.205 2.5 £ 1024 1.980 3.066 6.3 £ 1024 2.523 1.455

2 0.040 2 10.588 2 0.448 2 0.060 2 1.734 2 0.688 2 0.065 2 25.148 2 1.283 2 0.052 2 3.203 2 1.152

0.513 7.562 0.750 0.568 0.775 0.874 0.178 3.611 1.315 0.236 2.282 1.325

0.565 14.012 3.754 0.585 2.095 2.441 0.918 19.683 14.225 0.802 10.379 3.725

0.217

NC

0.781

0.224

NC

0.809

0.024

NC

0.942

0.066

NC

0.868

4.2 £ 1025 0.951 0.248 3.2 £ 1024 5.725 1.482 5.9 £ 1026 10.421 8.824 9.0 £ 1025 2.498 1.282

2 0.024 2 8.255 2 0.834 0.439 4.464 1.084 2 0.042 2 125.338 2 2.537 0.172 2.718 1.582

0.045 3.975 0.971 2 0.402 2 4.395 2 0.965 0.152 37.499 3.928 2 0.153 2 2.431 2 1.082

0.987 60.042 17.263 0.707 24.381 4.618 0.981 80.122 63.813 0.805 13.545 7.876

2 0.002

2 0.044

0.986

0.238

2 0.141

0.945

0.034

2 0.073

1.015

0.096

2 0.080

0.901

395

(continued)

Univariate and Multivariate Estimates

Financial

3.1 £ 1024 19.574 2.965 1.3 £ 1024 9.023 3.693

396

Table 6.4. Continued Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Libya

Economic

Financial

Composite

Morocco

Economic

Financial

Political

Composite

a

g

b

a þ g/2

Second

1.0 £ 10 1.589 1.245 8.2 £ 1024 8.339 147.529 4.9 £ 1024 12.049 3.950 5.1 £ 1024 6.981 57.5962

0.078 1.272 0.879 2 0.029 2 1.865 2 1.377 0.524 5.142 0.990 0.120 2.123 0.618

0.469 1.431 1.512 0.565 10.479 4.945 2 0.011 2 0.203 2 1.066 2 0.166 2 1.902 2 0.837

2 0.666

0.547

NC

0.536

NC

0.513

NC

2 0.046

8.4 £ 1024 2.258 2.351 4.5 £ 1024 0.614 74.861 1.6 £ 1024 0.737 15.368 4.7 £ 1025 1.015 4.504

0.068 1.318 0.717 2 0.014 2 0.852 2 0.217 2 0.011 2 2.545 2 0.143 2 0.012 2 3.654 2 0.171

0.003 0.006 0.006 0.564 0.796 3.012 0.757 2.251 5.005 0.904 9.171 9.017

2 4.499

0.070

NC

0.551

NC

0.746

NC

0.892

24

9.5 £ 10 2.323 1.296 8.4 £ 1024 0.433 1.246 1.8 £ 1024 6.850 1.132 3.6 £ 1024 6.513 2.026

0.006 0.180 0.079 0.010 0.191 0.289 2 0.025 2 2.071 2 1.339 2 0.017 2 0.394 2 2.774

0.141 1.919 0.612 2 0.056 2 0.490 2 2.007 0.583 8.539 0.667 0.434 4.564 0.554

0.513 2.511 1.602 0.568 0.567 1.448 0.562 12.054 1.555 0.055 0.544 0.133

1.0 £ 1023 13.794 4.357 6.9 £ 1024 0.498 1.043 8.4 £ 1027 0.376 0.029 3.9 £ 1024 1.556 2.286

2 0.082 2 5.762 2 3.950 2 0.008 2 0.170 2 1.098 2 0.011 2 2.801 2 0.330 0.194 1.359 0.860

0.266 1.615 1.711 2 0.045 2 2.116 2 3.083 0.096 2.733 0.275 2 0.272 2 0.864 2 1.184

0.018 0.264 0.392 0.415 0.354 0.570 0.983 116.222 15.847 0.368 0.943 1.369

Moment Log

Second

0.077

2 0.656

0.590

2 0.018

2 0.555

0.550

0.266

NC

0.828

0.200

NC

0.255

0.051

NC

0.069

2 0.031

NC

0.385

0.038

2 0.035

1.020

0.058

2 0.871

0.426

S. Hoti and M. McAleer

Political

23

v

Oman

Economic

Financial

Political

Qatar

Economic

Financial

Political

Composite

Saudi Arabia

Economic

Financial

2 0.021 2 6.095 2 0.357 2 0.012 2 0.364 2 2.217 0.058 1.312 1.036 2 0.033 2 2.683 2 1.484

0.945 43.294 28.752 0.565 0.482 1.129 2 0.460 2 1.213 2 0.913 0.599 2.062 5.177

1.4 £ 1023 8.904 117.372 6.5 £ 1024 1.062 44.518 2.1 £ 1024 2.382 19.255 1.7 £ 1025 5.130 17.340

0.233 3.645 0.858 2 0.023 2 14.277 2 0.812 2 0.029 2 1.304 2 1.131 2 0.026 2 12.435 2 0.893

1.7 £ 1024 2.453 0.910 9.0 £ 1025 12.032 1.798

0.006 0.534 0.182 0.860 4.494 3.033

NC

0.924

NC

0.553

NC

2 0.402

NC

0.566

2 0.155 2 1.379 2 0.848 0.575 1.434 3.583 0.572 3.123 5.154 0.966 83.013 22.248

NC

0.078

NC

0.552

NC

0.543

NC

0.940

0.901 21.519 7.691 0.451 16.046 3.050

2 0.098

0.907

2 0.446

1.311

1.2 £ 1024 5.129 8.390 8.2 £ 1025 1.312 1.166 1.3 £ 1024 1.464 20.911 1.0 £ 1024 1.306 233.449

2 0.034 2 64.021 2 1.943 0.003 0.186 0.330 2 0.028 2 1.288 2 0.579 2 0.034 2 1.638 2 1.701

0.089 5.132 0.737 2 0.084 2 1.605 2 2.499 0.175 1.121 1.124 2 0.005 2 0.155 2 0.081

0.937 54.962 31.737 0.828 6.063 4.872 0.111 0.185 0.520 0.542 1.511 4.329

7.4 £ 1024 3.978 2.268 6.6 £ 1024 1.019 28.141 1.9 £ 1024 42.966 223.806 1.7 £ 1024 1.179 3.783

2 0.042 2 2.714 2 3.542 0.010 0.105 0.168 2 0.025 2 1.224 2 1.053 2 0.028 2 0.720 2 0.163

0.530 3.890 1.154 2 0.033 2 0.359 2 0.522 2 0.027 2 0.514 2 0.179 2 0.015 2 0.379 2 0.095

0.346 2.214 1.376 0.575 1.375 3.792 0.566 23.653 3.858 0.572 1.552 5.344

2.1 £ 1024 5.090 1.797 9.4 £ 1025 9.316 1.908

2 0.055 2 8.656 2 2.990 1.100 2.715 2.262

0.103 3.490 1.653 2 0.414 2 1.222 2 0.480

0.891 34.691 13.043 0.428 9.254 3.013

0.011

NC

0.948

2 0.187

0.789

0.059

NC

0.170

2 0.037

NC

0.506

0.223

NC

0.569

2 0.007

2 0.545

0.568

2 0.038

NC

0.527

2 0.036

NC

0.536

2 0.004

NC

0.887

2 0.445

1.322

2 0.039

0.893

397

(continued)

Univariate and Multivariate Estimates

Composite

1.5 £ 1024 3.095 25.931 1.8 £ 1024 0.372 1.181 2.1 £ 1024 3.884 1.890 8.5 £ 1025 1.418 135.977

Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Political

Composite

Economic

Financial

Political

Composite

Tunisia

Economic

Financial

Political

25

v

a

g

b

a þ g/2

Second

7.8 £ 10 5.296 0.773 3.6 £ 1025 2.961 0.671

0.117 4.427 0.830 0.077 2.388 1.599

0.769 21.347 3.226 0.840 16.943 5.363

2 0.164

0.886

2 0.104

0.917

4.8 £ 1025 1.617 0.410 3.1 £ 1024 2.334 0.697 9.3 £ 1026 3.486 0.672 8.4 £ 1025 10.075 11.415

0.028 1.922 0.910 0.053 1.722 0.951 0.167 6.587 2.274 2 0.045 2 4.044 2 1.945

0.933 26.739 8.322 0.786 9.066 3.033 0.849 79.159 11.405 0.822 31.266 18.118

2 0.044

0.961

2 0.199

0.839

2 0.040

1.016

NC

0.777

8.7 £ 1024 10.890 3.142 1.9 £ 1024 19.499 2.181 5.9 £ 1025 8.496

0.379 4.120 1.497 2.123 10.902 1.109 0.678 5.838

2 0.109 2 1.361 2 1.390 0.110 3.427 0.861 0.530 16.804

NC

0.271

2 1.530

2.233

2 0.340

1.208

24

Moment Log

Second

2.4 £ 10 5.743 4.388 2 1.9 £ 1027 2 0.417 2 0.447

2 0.037 2 12.255 2 1.711 2 0.025 2 14.300 2 1.148

0.797 3.522 1.821 0.068 5.362 1.393

0.415 4.405 3.352 0.999 216.417 32.452

0.362

NC

0.776

0.010

2 0.034

1.008

1.5 £ 1023 16.677 3.755 3.4 £ 1024 1.246 1.732 1.1 £ 1026 2.944 1.576 2.2 £ 1024 2.972 2.023

2 0.006 2 0.359 2 0.225 2 0.019 2 2.268 2 2.472 2 0.018 2 11.468 2 1.624 2 0.055 2 8.589 2 5.306

0.500 3.356 0.886 0.088 1.385 1.376 0.330 6.333 3.260 0.453 1.665 1.930

2 0.177 2 3.734 2 0.858 0.838 6.384 8.958 0.943 169.446 35.181 0.496 3.160 2.066

0.245

NC

0.068

0.025

NC

0.863

0.147

2 0.083

1.090

0.171

NC

0.667

8.7 £ 1024 6.236 2.827 2.0 £ 1024 17.985 2.254 4.9 £ 1025 9.118

0.239 6.854 1.618 3.046 8.644 0.921 0.130 2.603

0.103 0.542 0.549 2 2.273 2 4.500 2 0.694 1.352 3.971

2 0.184 2 1.190 2 1.458 0.099 2.663 0.833 0.576 16.862

0.291

NC

0.107

1.910

2 1.414

2.009

0.805

2 0.435

1.382

S. Hoti and M. McAleer

Syria

398

Table 6.4. Continued

Composite

United Arab Emirates

Economic

Financial

Composite

Yemen

Economic

Financial

Political

Composite

1.471 0.563 4.297 1.939

2.274 0.657 13.690 6.375

3.0 £ 1024 2.532 0.968 1.8 £ 1025 3.701 1.078 2.1 £ 1025 8.196 0.719 3.0 £ 1026 8.378 1.190

0.089 1.809 0.863 0.091 3.854 2.230 0.218 6.065 1.789 2 0.011 2 34.390 2 1.016

0.302 1.116 0.508 0.892 41.922 25.505 0.806 40.661 7.788 1.005 511.768 52.791

6.3 £ 1023 0.784 0.392 4.8 £ 1023 3.887 159.500 4.0 £ 1024 0.785 1.215 7.1 £ 1024 0.663 1.002

2 0.018 2 0.580 2 1.430 0.226 0.554 2.024 2 0.024 2 0.751 2 2.369 2 0.025 2 0.675 2 2.896

0.577 1.161 0.453 2 0.202 2 0.652 2 0.607 0.629 1.312 1.493 0.589 0.925 1.507

2 0.135

1.220

2 1.035

0.390

2 0.054

0.983

2 0.107

1.023

2 0.020

0.994

NC

0.560

NC

0.024

NC

0.605

NC

0.565

1.192 2.3 £ 1025 3.401 0.749

1.012 0.202 4.333 0.934

1.343 0.655 2.093 1.000

2.574 0.682 13.863 4.294

5.2 £ 1025 3.872 0.451 2.4 £ 1025 5.118 1.250 1.7 £ 1025 6.321 0.634 2.4 £ 1026 2.250 1.697

2 0.015 2 1.659 2 0.901 0.127 3.891 1.902 0.006 0.847 0.167 2 0.014 2 12.068 2 1.334

0.095 2.517 0.967 2 0.133 2 4.304 2 1.780 0.578 4.935 1.382 0.316 5.164 1.095

6.5 £ 1023 0.469 1.113 4.5 £ 1025 3.826 0.492 2.0 £ 1024 0.914 0.694 3.0 £ 1024 1.864 0.455

0.029 0.181 0.180 2 0.036 2 20.550 2 0.811 2 0.028 2 1.630 2 1.107 2 0.031 2 6.369 2 0.507

2 0.047 2 0.277 2 0.308 0.134 3.815 1.709 0.021 0.653 0.170 0.134 1.089 0.491

0.529

2 0.231

1.212

0.876 29.123 3.187 0.888 45.028 22.270 0.850 31.545 6.592 0.946 72.702 36.603

0.032

2 0.154

0.908

0.060

2 0.038

0.949

0.295

2 0.155

1.145

0.144

2 0.080

1.090

0.573 0.615 1.801 0.965 73.920 6.357 0.857 4.888 4.292 0.813 6.861 1.865

0.005

2 0.534

0.578

0.031

NC

0.997

2 0.017

NC

0.840

0.036

NC

0.850

399

Notes: (1) NC denotes that the log-moment could not be calculated because [(a þ gI(ht))h2t þ b] in (7) or (ah2t þ b) in (8) was negative for one observation. (2) The three entries for each parameter are their respective estimate, the asymptotic t-ratio and the Bollerslev and Wooldridge (1992) robust t-ratio.

Univariate and Multivariate Estimates

Political

1.229 2.5 £ 1025 3.498 1.068

400

Table 6.5. Univariate GARCH(1,1) and GJR(1,1) estimates for North and Central America by risk return Country

Risk Returns

GARCH(1,1)

v

Bahamas

Economic

Political

Composite

Canada

Economic

Finance

Political

Composite

b

Moment Log

Second

1.3 £ 1024 2.812 0.822 2.2 £ 1024 0.778 11.314 1.2 £ 1025 6.862 1.445 2.3 £ 1025 1.277 14.428

0.124 1.757 1.572 20.009 20.786 20.079 1.266 3.320 1.247 20.019 27.619 20.668

0.378 1.754 0.623 0.592 1.129 2.119 0.342 5.496 2.601 0.612 1.954 4.056

2 0.792

0.503

NC

0.583

2 0.769

1.609

NC

0.592

8.7 £ 1027 0.535 0.351 2.0 £ 1029 0.001 0.001 9.1 £ 1026 9.262 21.820 2.0 £ 1025 4.573 54.781

0.062 3.803 2.019 20.003 21.949 20.117 20.052 230.539 23.012 20.115 225.732 23.328

0.949 47.078 31.971 1.001 58.712 30.254 0.926 64.566 29.758 0.597 5.226 7.634

2 0.002

1.012

2 0.003

0.998

NC

0.875

NC

0.482

v

a

g

b

1.3 £ 1024 3.606 4.854 2.2 £ 1024 0.775 7.475 1.2 £ 1025 9.138 1.458 2.0 £ 1025 1.564 6.259

20.089 27.383 24.556 0.020 0.107 0.122 1.439 3.890 1.224 20.031 24.391 22.515

0.267 3.469 1.423 20.029 20.154 20.109 21.048 20.819 20.751 0.011 9.218 0.210

0.436 2.771 3.926 0.596 1.143 2.558 0.332 5.475 2.695 0.664 2.951 4.628

4.1 £ 1027 0.212 0.145 2.0 £ 1025 24.027 10.019 3.0 £ 1025 15.436 548.467 2.8 £ 1025 3.632 112.001

0.033 2.021 1.112 20.093 28.001 20.877 20.067 2 11.638 25.569 20.118 26.253 23.244

0.065 1.994 0.623 0.090 7.422 0.963 0.068 2.163 1.559 0.017 0.452 0.391

0.952 42.528 31.053 0.925 252.693 12.238 0.656 158.841 9.127 0.439 2.281 4.890

a þ g/2

Moment Log

Second

0.044

NC

0.480

0.006

20.507

0.601

0.915

20.780

1.247

NC

0.639

0.066

20.020

1.018

2 0.048

20.110

0.876

2 0.033

NC

0.624

2 0.109

NC

0.330

2 0.025

S. Hoti and M. McAleer

Finance

a

GJR(1,1)

Costa Rica

Economic

Finance

Political

Cuba

Economic

Finance

Political

Composite

Dominican Republic

Economic

Finance

0.037 1.100 0.691 0.822 18.923 5.211 1.027 78.060 35.907 0.627 5.708 2.795

21.583

1.016

20.135

0.893

20.007

1.002

20.345

0.737

3.4 £ 1024 1.050 0.441 5.3 £ 1024 1.425 0.571 6.3 £ 1025 0.685 19.438 3.5 £ 1025 6.919 16.430

20.004 20.394 20.081 0.064 1.115 0.885 20.023 20.799 20.374 20.026 2 10.655 20.711

0.783 3.649 1.605 0.615 2.282 1.296 0.681 1.449 4.960 0.919 70.005 16.799

20.250

0.779

20.436

0.679

NC

0.658

NC

0.893

8.0 £ 1024 11.781 4.411 1.1 £ 1023 17.996 3.120

0.373 3.427 2.161 0.278 8.674 1.889

20.081 23.503 21.939 20.150 23.736 21.660

NC

0.292

NC

0.128

4.6 £ 1024 11.574 3.805 2.1 £ 1025 4.282 1.100 2.5 £ 1026 2.606 1.547 4.4 £ 1025 3.882 1.515

1.551 4.569 1.674 0.063 3.589 0.978 20.025 28.072 20.701 0.242 2.206 1.625

2 1.137 2 2.462 2 1.237 2 0.097 2 4.750 2 1.066 0.042 5.725 0.716 2 0.214 2 1.361 2 1.526

0.001 0.100 0.065 0.864 28.830 7.340 0.989 118.532 28.302 0.484 4.426 1.694

8.3 £ 1024 2.815 0.848 6.6 £ 1024 1.515 1.854 9.3 £ 1025 1.061 4.154 1.8 £ 1024 20.151 63.587

0.059 17.301 0.769 20.047 26.219 23.211 20.024 20.615 20.321 20.024 20.647 20.361

2 0.104 2 2.426 2 1.460 0.163 2.189 1.346 2 0.091 2 1.726 2 1.771 2 0.030 2 0.346 2 0.477

0.552 3.730 0.948 0.736 4.186 4.567 0.561 1.247 6.508 0.524 16.221 3.441

8.3 £ 1024 12.758 4.689 7.4 £ 1024 3.793 627.910

0.306 2.911 1.103 20.049 25.888 23.410

0.156 0.649 0.488 0.398 2.632 1.485

20.074 27.214 21.694 0.364 2.184 2.474

0.983

2 2.424

0.984

0.014

2 0.092

0.879

20.004

2 0.040

0.985

0.135

2 0.469

0.619

0.007

2 0.539

0.559

0.034

NC

0.771

20.069

NC

0.492

20.039

NC

0.485

0.384

NC

0.309

0.150

NC

0.515

(continued)

401

0.979 7.464 2.007 0.071 3.477 1.183 20.025 2 36.768 21.293 0.110 2.368 1.536

Univariate and Multivariate Estimates

Composite

4.2 £ 1024 10.796 3.242 2.4 £ 1025 3.729 0.683 6.2 £ 1027 13.777 0.737 3.0 £ 1025 2.959 1.362

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Political

Composite

Economic

Finance

Political

Composite

Guatemala

Economic

Finance

25

6.6 £ 10 15.153 754.993 1.7 £ 1024 11.287 798.214

2 0.044 2 2.935 2 4.323 0.385 5.553 1.763

0.723 80.537 11.164 20.088 22.717 22.280

3.1 £ 1026 3.263 5.873 2.0 £ 1024 2.371 1.029 7.9 £ 1025 4.136 1.045 8.9 £ 1026 2.748 1.318

2 0.015 211.697 2 0.438 0.057 1.472 0.698 0.059 2.650 0.756 0.021 3.090 0.488

1.002 402.004 24.498 0.582 3.289 2.185 0.720 11.821 3.132 0.932 68.820 15.750

9.4 £ 1025 3.253 0.919 7.0 £ 1027 0.068 0.005

0.115 3.509 1.359 2 0.004 2 3.987 2 0.257

0.828 17.034 8.601 1.002 109.903 6.666

v

a

g

b

a þ g/2

Second

NC

0.679

NC

0.296

2 0.019

0.986

2 0.492

0.639

2 0.264

0.779

2 0.050

0.953

2 0.098

0.942

2 0.002

0.998

25

Moment Log

Second

6.6 £ 10 85.439 1152.484 1.6 £ 1024 8.384 193.039

20.043 25.591 24.179 0.481 3.612 1.451

20.075 23.463 23.760 20.143 20.884 20.457

0.728 41.842 10.083 20.082 21.818 21.796

2 0.080

NC

0.648

0.409

NC

0.327

1.8 £ 1024 3.007 0.669 4.8 £ 1024 3.250 1.190 3.1 £ 1024 11.403 3.465 1.4 £ 1024 7.045 1.783

0.038 1.373 0.457 0.186 1.411 1.241 0.820 4.274 1.401 0.452 3.285 1.582

0.123 1.686 0.722 20.248 21.493 21.590 20.718 23.247 21.218 20.506 23.335 21.800

0.723 8.460 2.379 0.478 2.720 1.191 0.021 0.345 0.344 0.313 3.931 0.956

0.100

20.280

0.823

0.062

20.644

0.540

0.461

22.236

0.483

0.199

20.658

0.512

1.3 £ 1024 3.807 1.051 1.9 £ 1025 6.053 0.638

0.272 3.479 1.800 20.011 215.942 20.999

20.303 23.656 21.982 0.107 3.512 1.307

0.812 17.128 6.203 0.988 38.351 22.347

0.120

NC

0.932

0.042

20.030

1.030

S. Hoti and M. McAleer

El Salvador

402

Table 6.5. Continued Country

Political

Composite

Haiti

Economic

Political

Composite

Honduras

Economic

Finance

Political

Composite

20.060 27.380 22.193 20.044 28.214 24.271

0.509 2.514 5.543 0.853 57.311 23.196

NC

0.448

NC

0.809

2.9 £ 1024 3.854 1.117 1.1 £ 1023 1.424 1.390 1.1 £ 1023 10.202 3.618 2.4 £ 1024 3.517 2.496

0.151 3.292 1.163 20.042 22.162 24.879 0.538 4.392 2.083 0.349 3.750 1.923

0.530 4.804 1.563 0.600 2.053 1.794 20.032 20.395 20.824 0.393 3.025 2.588

20.468

0.681

NC

0.558

NC

0.507

20.537

0.742

1.2 £ 1023 1.948 0.733 2.2 £ 1024 21.232 211.333 6.3 £ 1025 0.769 0.211 8.3 £ 1025 1.342 1.880

20.022 22.073 23.817 2.234 9.402 1.759 0.013 0.721 0.369 0.005 0.278 0.119

0.425 1.444 0.519 20.045 21.665 23.031 0.791 2.943 0.858 0.659 2.637 4.195

NC

0.404

NC

2.190

20.220

0.804

20.411

0.663

2.0 £ 1024 0.752 1.300 4.7 £ 1025 4.794 25.453

0.047 0.491 0.628 20.050 23.853 20.980

2 0.121 2 0.685 2 1.741 0.034 1.111 0.679

0.468 0.649 1.136 0.899 56.929 22.986

20.014

2 0.698

0.454

20.033

NC

0.866

2.9 £ 1024 7.042 2.144 1.5 £ 1023 2.995 1.583 1.1 £ 1023 10.151 3.601 1.7 £ 1024 2.520 2.295

20.062 22.894 24.812 20.058 2 11.788 24.686 0.533 3.201 1.691 0.144 1.423 1.843

0.277 3.186 2.669 0.213 2.442 0.984 0.010 0.050 0.021 0.305 1.913 1.309

0.791 48.817 7.801 0.476 2.861 1.396 20.031 20.389 20.692 0.530 3.605 4.406

0.077

NC

0.868

0.049

NC

0.525

0.538

NC

0.507

0.297

2 0.456

0.827

1.2 £ 1023 3.307 2.458 3.0 £ 1024 17.405 188.015 9.0 £ 1025 2.140 0.619 1.9 £ 1024 2.633 126.773

20.077 21.512 23.116 1.952 6.121 1.042 0.022 1.241 0.440 0.003 0.085 0.061

0.280 1.283 1.446 2 1.099 2 2.509 2 0.569 2 0.130 2 3.249 2 2.004 2 0.037 2 0.911 2 0.336

0.531 3.946 2.525 20.085 24.346 23.784 0.727 5.291 1.689 0.295 1.082 1.803

0.063

NC

0.594

1.403

NC

1.318

20.043

2 0.294

0.684

20.015

2 1.209

0.280

403

(continued)

Univariate and Multivariate Estimates

Finance

1.4 £ 1024 2.676 341.820 5.9 £ 1025 12.390 227.981

404

Table 6.5. Continued Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Jamaica

Economic

Political

Composite

Mexico

Economic

Finance

Political

Composite

1.5 £ 10 3.666 261.243 3.4 £ 1025 1.488 0.360 1.1 £ 1024 1.409 8.816 2.3 £ 1024 5.903 1463.719

20.033 28.139 22.190 0.031 1.457 0.803 20.025 20.888 20.316 0.102 2.405 2.268

0.932 35.254 34.181 0.878 10.876 3.403 0.311 0.646 2.064 20.580 22.608 23.319

2.6 £ 1024 1.441 1.871 4.5 £ 1025 6.251 1.355 6.5 £ 1025 1.456 1.177 1.4 £ 1024 2.052 10.885

0.089 1.545 1.305 20.008 22.128 20.434 0.076 1.869 1.055 20.034 24.417 20.653

0.698 3.792 5.710 0.941 95.469 57.605 0.667 3.142 3.013 0.476 1.802 3.863

a

g

b

a þ g/2

Second

NC

0.899

20.100

0.909

NC

0.286

NC

2 0.478

20.262

0.787

20.070

0.933

20.324

0.742

NC

0.442

23

1.3 £ 10 0.969 1.551 1.3 £ 1024 1.941 0.811 1.7 £ 1026 1.855 2.378 2.1 £ 1025 2.523 0.826

0.101 2.975 0.507 0.067 4.574 0.748 2 0.011 2 2.693 2 0.256 0.083 2.694 0.981

20.147 23.000 20.750 20.094 23.304 21.106 20.012 21.236 20.287 20.119 22.678 21.120

0.374 0.590 0.869 0.643 3.708 1.550 1.005 167.181 27.236 0.836 13.128 4.714

1.6 £ 1024 2.071 1.546 3.1 £ 1025 20.457 4.912 7.4 £ 1025 1.387 1.207 1.6 £ 1024 2.571 133.236

2 0.014 2 0.768 2 0.318 2 0.082 258.678 2 1.059 0.028 0.654 0.555 2 0.043 2 0.786 2 0.659

0.256 2.989 1.311 0.069 22.980 1.355 0.122 1.102 1.059 0.010 0.193 0.135

0.771 8.040 7.945 0.989 52.909 23.494 0.625 2.546 2.505 0.408 1.716 2.951

Moment Log

Second

0.028

20.862

0.402

0.020

20.370

0.663

20.017

20.008

0.988

0.024

20.095

0.860

0.114

20.281

0.884

NC

0.942

20.433

0.714

NC

0.370

20.048

0.089

20.038

S. Hoti and M. McAleer

Finance

24

v

Nicaragua

Economic

Finance

Political

Panama

Economic

Finance

Political

Composite

Trinidad and Tobago

Economic

Finance

0.085 4.080 2.109 0.191 2.401 0.709 0.183 2.926 2.286 0.541 4.325 2.286

0.907 46.235 29.864 20.071 20.717 20.610 0.685 8.171 4.744 0.157 2.622 0.889

20.028

0.992

NC

0.120

20.229

0.869

21.112

0.698

4.5 £ 1025 4.397 0.799 1.4 £ 1024 7.490 1.077 9.4 £ 1025 5.211 1.482 4.0 £ 1025 3.054 0.550

0.137 3.881 1.387 2 0.008 2 0.841 2 0.323 0.261 3.268 1.025 0.026 1.536 0.487

0.729 15.493 2.872 0.719 19.627 2.769 0.599 9.026 3.364 0.730 8.907 1.589

20.191

0.866

20.344

0.710

20.344

0.860

20.284

0.756

4.1 £ 1024 0.596 0.374 9.8 £ 1026 6.704 0.868

2 0.019 2 0.624 2 0.639 0.077 7.762 0.919

0.445 0.473 0.283 0.904 96.967 13.880

20.880

0.426

20.053

0.981

2.3 £ 1024 4.115 1.531 3.8 £ 1023 1.089 1.041 7.6 £ 1025 3.229 0.465 3.5 £ 1024 8.166 3.268

2 0.041 2 5.893 2 1.854 0.172 1.882 0.337 0.022 0.777 0.801 1.132 4.058 2.189

0.161 7.122 2.891 20.187 21.580 20.367 0.161 2.018 1.025 21.085 24.001 22.051

0.956 73.527 30.913 0.274 0.413 0.425 0.791 13.262 2.281 0.174 2.634 1.295

0.039

NC

0.995

0.079

21.182

0.353

0.102

20.210

0.893

0.590

20.701

0.764

5.2 £ 1025 4.521 0.886 2.6 £ 1024 10.604 127.473 1.1 £ 1025 4.024 1.786 3.0 £ 1025 2.512 0.759

0.208 3.245 1.264 2 0.035 2 9.470 2 0.861 0.413 2.667 0.959 0.016 1.055 0.287

20.103 21.397 20.567 0.009 0.565 0.192 20.299 22.302 20.626 0.058 1.913 0.366

0.690 13.571 2.608 0.623 20.348 5.464 0.550 5.705 3.567 0.778 9.919 3.107

0.156

20.197

0.846

NC

0.593

0.264

20.367

0.814

0.045

20.232

0.823

1.8 £ 1024 1.016 0.614 2.5 £ 1024 11.605 24.311

2 0.014 2 0.638 2 0.391 2 0.084 2 1.704 2 1.066

0.065 1.321 0.804 0.334 2.105 1.717

0.724 2.707 1.725 0.412 9.320 2.286

0.019

20.344

0.743

0.084

NC

0.496

20.030

405

(continued)

Univariate and Multivariate Estimates

Composite

2.3 £ 1024 2.567 0.597 4.5 £ 1023 9.455 2.192 9.7 £ 1025 2.668 1.559 3.8 £ 1024 7.802 2.693

406

Table 6.5. Continued Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Political

Composite

Economic

Finance

Political

Composite

3.0 £ 10 8.893 43.906 3.6 £ 1025 1.566 0.731

0.337 4.777 1.850 0.068 1.326 1.235

20.123 21.456 21.409 0.704 3.854 2.358

6.6 £ 1025 6.994 2.475 2.6 £ 1024 4.911 0.981 5.5 £ 1025 1.331 1.012 5.5 £ 1027 0.643 0.171

0.590 5.394 2.442 0.676 1.906 1.117 0.060 1.766 0.820 0.037 3.218 1.350

0.328 5.455 2.645 0.505 5.133 5.500 0.653 2.752 1.931 0.968 55.072 18.746

a

g

b

a þ g/2

Second

NC

0.214

20.281

0.772

20.568

0.918

20.539

1.181

20.362

0.712

20.002

1.005

24

2.1 £ 10 20.010 48.495 8.1 £ 1026 2.473 1.726

1.351 6.378 2.178 0.010 0.705 0.304

21.084 23.939 21.732 0.033 1.120 0.472

2 0.073 2 5.381 2 2.411 0.926 40.117 14.392

7.0 £ 1025 6.939 2.705 2.1 £ 1024 3.171 1.029 1.7 £ 1025 3.178 1.381 2.4 £ 1027 0.242 0.106

0.877 3.970 1.927 1.001 2.103 0.585 2 0.059 2 5.337 2 2.160 0.046 2.849 1.090

20.439 21.774 20.839 20.779 22.032 20.443 0.109 4.457 1.545 20.029 21.156 20.316

0.287 4.730 2.378 0.594 4.865 5.641 0.918 31.845 16.289 0.977 50.994 27.010

Moment Log

Second

0.809

NC

0.737

0.027

20.066

0.953

0.658

20.543

0.945

0.611

20.367

1.205

NC

0.914

0.013

1.009

20.005

0.031

Notes: (1) NC denotes that the log-moment could not be calculated because [(a þ gI(ht))h2t þ b] in (7) or (ah2t þ b) in (8) was negative for one observation. (2) The three entries for each parameter are their respective estimate, the asymptotic t-ratio and the Bollerslev and Wooldridge (1992) robust t-ratio.

S. Hoti and M. McAleer

USA

24

v

Table 6.6. Univariate GARCH(1,1) and GJR(1,1) estimates for South America by risk return Country

Risk Returns

GARCH(1,1)

v

Argentina

Economic

Political Composite

Bolivia

Economic Finance Political

b

Moment Log

Second

2 0.031

0.981

2 0.113

1.005

2 0.116

0.891

0.073 4.032 1.580 0.259 6.379 0.815 0.022 1.350 0.556 0.176 4.612 1.689

2 0.107

0.937

2.4 £ 1024 2.861 0.501 3.5 £ 1025 16.002 1.226 2.4 £ 1024 3.933 2.024

0.052 0.867 2 0.094 2.735 20.864 1.057 4.661 0.004 0.931 2 0.067 1.297 257.204 0.209 35.542 0.176 0.120 2 1.650 2.312 0.598 1.057 0.383

0.919

0.908 44.356 16.796 0.746 27.777 3.370 0.869 18.046 10.044 0.760 17.089 6.985

0.935 0.296

a

g

b

a þ g/2

Moment Log

6.2 £ 1025 0.014 2.344 0.761 0.881 0.109 1.7 £ 1024 0.174 4.101 2.949 0.979 0.784 1.4 £ 1025 2 0.060 3.628 2 8.594 30.198 2 2.800 2.7 £ 1025 0.067 3.111 1.328 1.523 1.280 2.0 £ 1024 2.449 0.450 4.1 £ 1023 5.262 4.064 1.6 £ 1024 3.827 1.949

0.060 1.848 0.806 0.230 0.670 1.620 0.246 3.153 1.126

0.109 2.569 0.595 0.137 2.554 0.419 0.127 4.161 2.649 0.182 2.749 1.212

Second

0.923 55.176 16.932 0.746 25.130 3.419 0.964 67.062 53.382 0.789 18.435 10.288

0.068 2 0.066

0.991

0.243 2 0.154

0.988

0.004

0.968

0.158 2 0.163

0.947

2 0.029 0.887 2 0.736 21.883 2 0.270 5.330 0.729 2 0.016 1.611 2 0.090 0.139 2 0.324 2 0.291 0.391 2 3.237 2.858 2 1.305 1.332

0.045 2 0.067

0.932

0.594

0.578

NC

NC

0.101 2 0.651

0.492

(continued)

407

9.5 £ 1025 2.391 1.030 1.5 £ 1024 3.925 1.000 3.9 £ 1025 2.523 1.316 3.1 £ 1025 3.365 1.304

v

Univariate and Multivariate Estimates

Finance

a

GJR(1,1)

Country

Risk Returns

GARCH(1,1)

v

a

b

GJR(1,1) Moment Log

a

g

b

a þ g/2

Second

Moment Log

Second

NC

1.002

Composite

6.8 £ 1026 2.170 0.717

0.039 3.196 1.539

0.935 2 0.027 55.900 21.644

0.973

4.7 £ 1027 20.038 0.370 23.144 0.887 22.021

Economic

1.3 £ 1023 2.302 1.059 5.2 £ 1025 3.481 1.151 1.9 £ 1024 3.003 2.127 2.2 £ 1024 3.891 1.979

0.086 1.865 0.755 0.078 4.457 1.087 0.211 3.127 1.281 0.193 3.233 0.906

0.211 0.665 0.309 0.897 39.977 12.365 0.152 0.621 0.527 0.134 0.697 0.443

2 1.321

0.297

0.977

0.362

0.116 22.268

0.146

2 1.452

0.327

0.310 1.141 0.660 0.900 33.475 12.152 0.030 0.101 0.154 0.031 0.085 0.122

0.077 20.033

2 1.367

2 0.173 2 1.483 2 1.076 2 0.025 2 0.532 2 0.159 2 0.300 2 2.765 2 1.034 2 0.263 2 1.994 2 0.691

0.390

0.976

0.166 1.728 1.086 0.089 3.551 0.681 0.266 2.551 0.906 0.250 3.200 0.655

0.080 20.885

2 0.043

1.1 £ 1023 2.346 1.397 5.0 £ 1025 2.745 1.049 2.5 £ 1024 3.095 3.166 2.7 £ 1024 2.634 2.586

0.118 22.109

0.149

0.777 2 0.091 17.807 10.346 0.887 2 0.046 66.358 7.918 0.827 2 0.228 12.492

0.982

3.9 £ 1025 2.134 1.085 7.5 £ 1026 6.665 0.498 6.1 £ 1025 0.869

0.053 1.697 0.620 0.055 3.508 0.843 0.002 0.083

0.192 3.088 1.649 0.063 2.382 0.740 0.103 0.960

0.841 31.087 10.322 0.901 64.979 8.399 0.697 2.094

0.149 20.120

0.990

0.086 20.056

0.987

0.053 20.358

0.750

Finance Political Composite

Chile

v

Economic Finance Political

7.3 £ 1025 0.205 2.424 3.998 1.952 2.266 9.0 £ 1026 0.090 7.380 5.590 0.539 1.120 4.1 £ 1025 2 0.026 2.797 2 2.592

0.977 0.800

0.063 5.555 1.962

1.008 2 0.006 60.747 62.067

S. Hoti and M. McAleer

Brazil

408

Table 6.6. Continued

Composite

Colombia

Economic Finance

Composite

Ecuador

Economic Finance Political Composite

10.469 0.876 20.046 28.596 13.427

3.7 £ 1025 1.941 0.694 7.5 £ 1025 2.807 1.126 1.9 £ 1025 3.068 1.544 6.3 £ 1026 2.069 0.728

0.935 38.016 15.527 0.780 11.898 5.477 0.787 21.429 16.581 0.850 42.521 9.697

0.037 2.872 1.155 0.095 3.917 0.979 0.239 4.724 2.359 0.163 4.977 1.646

0.970

0.973

20.161

0.876

20.062

1.026

20.029

1.013

0.946 20.075 1.1 £ 1024 20.016 2.792 22.009 45.306 1.122 21.009 16.602 4.5 £ 1025 0.028 0.934 20.037 4.192 5.160 108.659 0.825 0.874 13.793 1.6 £ 1025 0.065 0.900 20.050 2.699 2.795 29.214 0.683 1.566 10.390 0.216 0.635 20.236 6.5 £ 1025 2.857 3.535 7.287 1.567 1.785 3.871

0.929 0.962 0.964 0.851

0.034 0.062 2.235 1.112

2.7 £ 1025 1.592 0.438 9.6 £ 1027 0.378 0.424 1.2 £ 1025 3.170 1.674 4.4 £ 1026 3.070 0.876

0.003 0.313 0.072 2 0.040 2 6.452 2 1.178 0.012 0.779 0.285 2 0.059 2 5.282 2 1.158

5.1 £ 1024 1.831 2.300 3.2 £ 1023 0.229 0.865 1.1 £ 1025 2.325 0.480 1.1 £ 1025 68.460 78.046

2 0.009 2 1.082 2 0.170 0.012 0.158 0.064 2 0.012 2 1.200 2 0.232 2 0.122 2 7.649 2 6.122

0.971 0.045 1.623 0.625

3.320 0.893 31.658 15.024

0.051 0.950 3.233 39.402 0.624 13.327 0.084 1.003 6.877 172.336 1.461 89.914 0.295 0.850 5.326 33.593 1.586 20.526 0.160 0.966 8.546 59.353 2.163 27.048

0.085 2 0.054

0.977

0.029 2 0.048

0.979

0.002 2 0.042

1.005

0.159 2 0.148

1.010

0.021

0.988

NC

2 0.057 0.794 20.038 2 0.242 2 2.865 5.902 2 1.136 7.664 2 0.021 0.137 0.001 2 1.946 2 0.089 0.036 2 0.117 0.139 0.164 0.917 0.070 2 0.100 2.859 30.235 1.293 9.610 0.284 0.957 0.020 NC 8.281 109.965 6.167 99.678

0.756 0.139 0.987 0.977

(continued)

409

20.030

1.186 4.8 £ 1026 2.878 0.804

Univariate and Multivariate Estimates

Political

2.405 20.948 6.3 £ 1026 0.093 2.816 3.110 0.909 2.146

Country

Risk Returns

GARCH(1,1)

v

Paraguay

Economic

Political Composite

Peru

Economic Finance Political Composite

a

b

GJR(1,1) Moment Log

Second

20.031

0.973

22.278

0.251

20.067

0.945

3.9 £ 1025 1.014 0.643 5.1 £ 1024 3.719 1.523 2.1 £ 1025 3.366 1.066 7.2 £ 1025 1.666 0.710

0.035 2.867 0.857 0.183 2.290 1.110 0.047 3.378 0.916 0.076 1.469 1.004

0.938 28.860 14.243 0.068 0.274 0.533 0.898 35.714 9.507 0.630 3.005 1.393

20.368

0.707

8.9 £ 1025 2.782 1.092 2.2 £ 1024 7.209 1.611 1.7 £ 1024 1.383 0.737 2.3 £ 1026

0.082 2.817 1.841 0.334 5.580 2.409 0.012 0.997 0.296 0.048

0.884 20.053 25.721 15.335 0.664 NC 18.806 8.661 0.818 20.188 6.272 4.591 0.942 20.016

0.966 0.998 0.830 0.990

v

a

6.6 £ 1025 0.727 0.481 5.5 £ 1024 2.404 0.993 7.4 £ 1025 1.959 1.258 7.1 £ 1025 1.948 0.976

0.014 0.519 0.507 2 0.047 2 2.827 2 0.566 2 0.007 2 0.266 2 0.161 2 0.010 2 0.338 2 0.137

1.1 £ 1025 1.147 0.518 2.2 £ 1024 6.311 1.544 1.4 £ 1024 9.958 2.465 2.5 £ 1026

2 0.003 2 0.461 2 0.092 0.457 5.349 1.689 2 0.103 2 5.016 2 2.343 0.092

g

0.047 1.470 0.572 0.190 2.348 0.485 0.143 1.628 0.950 0.151 1.460 1.133

b

0.919 11.742 9.479 0.272 0.900 0.334 0.717 5.304 3.331 0.644 3.544 1.973

a þ g/2

Moment Log

Second

0.038

NC

0.956

0.048

NC

0.320

0.065 20.343

0.782

0.065 20.457

0.709

0.192 0.928 0.093 20.078 3.908 74.533 2.276 30.942 2 0.190 0.653 0.362 20.161 2 1.840 17.298 2 0.508 8.494 0.133 0.920 2 0.036 NC 3.980 156.280 1.606 15.147 2 0.079 0.936 0.053 0.013

1.021 1.015 0.884 0.989

S. Hoti and M. McAleer

Finance

410

Table 6.6. Continued

0.579 0.572 Uruguay

Economic Finance

Composite

Venezuela Economic Finance Political Composite

2.1 £ 1024 1.578 0.877 3.0 £ 1024 2.159 13.313 1.8 £ 1025 4.483 0.515 7.9 £ 1025 5.285 4.092

2 0.023 2 1.112 2 1.123 2 0.012 2 0.310 2 0.078 0.044 3.179 0.762 0.496 4.771 1.769

6.8 £ 1025 2.174 0.553 5.6 £ 1024 4.858 2.595 8.8 £ 1025 2.568 0.697 9.7 £ 1025 1.636 0.507

0.033 2.498 0.922 0.289 3.768 1.435 0.043 1.829 0.717 0.045 1.490 0.540

54.282 45.833

0.565 0.457

0.799 20.258 6.105 3.355 0.587 NC 3.096 2.619 0.812 20.167 20.950 2.747 0.060 21.554 0.595 0.552 0.934 42.582 10.735 0.214 1.615 1.017 0.769 8.868 2.948 0.682 3.733 1.204

0.776 0.574 0.856 0.556

20.037

0.967

21.094

0.502

20.220

0.812

20.326

0.727

3.426 2 2.609 1.494 2 0.899

55.620 42.196

3.6 £ 1024 2 0.042 0.108 2.273 2 1.219 1.490 0.822 2 3.012 1.023 2.8 £ 1024 0.030 2 0.041 0.578 0.284 2 0.388 5.233 0.183 2 0.130 1.8 £ 1025 0.053 2 0.043 4.769 3.061 2 1.207 0.528 0.715 2 0.332 9.0 £ 1025 0.468 2 0.423 5.120 4.100 2 2.298 2.662 0.993 2 0.908

0.602 3.412 1.302 0.581 0.806 1.939 0.810 21.723 2.733 0.098 0.737 0.460

4.1 £ 1025 4.292 1.268 5.9 £ 1024 4.804 2.657 8.3 £ 1025 5.172 0.878 1.0 £ 1024 1.855 0.747

2 0.052 0.132 0.984 2 9.919 10.248 237.345 2 2.249 3.458 32.249 0.358 2 0.174 0.192 3.315 2 1.260 1.391 1.211 2 0.572 0.912 2 0.028 0.344 0.726 2 1.389 4.007 13.826 2 1.584 1.150 3.842 2 0.005 0.117 0.660 2 0.131 1.554 3.725 2 0.059 0.932 1.595

0.012

NC

0.614

0.009 2 0.523

0.590

0.031 2 0.163

0.841

0.256 2 1.352

0.354

0.014

0.998

NC

0.271 2 1.114

0.463

0.144

0.870

NC

0.054 2 0.423

Univariate and Multivariate Estimates

Political

2.555 1.788

0.714

411

Notes: (1) NC denotes that the log-moment could not be calculated because [(a þ gI(ht))h2t þ b] in (7) or (ah2t þ b) in (8) was negative for one observation. (2) The three entries for each parameter are their respective estimate, the asymptotic t-ratio and the Bollerslev and Wooldridge (1992) robust t-ratio.

Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Angola

Economic

Financial

Composite

Botswana

Economic

Financial

Political

Composite

v

a

g

b

a þ g/2

Second

1.5 £ 10 11.581 3.041 4.3 £ 1025 2.371 0.706 1.3 £ 1024 3.213 0.537 2.6 £ 1025 1.273 0.667

0.412 4.755 1.487 0.216 5.618 2.096 0.125 2.458 0.735 0.041 2.048 1.238

0.026 0.464 0.223 0.865 36.579 13.801 0.744 10.959 2.091 0.929 22.243 12.267

2 2.578

0.438

2 0.028

1.081

2 0.190

0.869

2 0.037

0.970

1.3 £ 1024 14.592 42.847 5.3 £ 1025 4.587 1.357 2.6 £ 1025 0.841 0.755 3.0 £ 1025 1.752 61.826

2 0.059 2 3.162 2 2.615 2 0.034 2 5.935 2 2.646 0.030 1.322 0.435 2 0.038 2 1.730 2 2.144

0.565 375.894 7.749 0.886 28.127 8.422 0.733 2.468 2.242 0.626 2.726 7.049

NC

0.506

NC

0.852

2 0.278

0.763

NC

0.588

23

Moment Log

Second

1.5 £ 10 10.393 2.928 4.6 £ 1025 3.269 0.841 1.4 £ 1024 2.991 1.115 2.7 £ 1025 1.556 0.618

0.508 3.023 1.030 0.386 5.804 1.641 0.036 0.824 0.703 2 0.014 2 0.741 2 0.351

2 0.197 2 0.854 2 0.364 2 0.298 2 4.570 2 1.030 0.393 4.199 0.729 0.082 2.349 1.439

0.027 0.413 0.208 0.851 39.437 13.578 0.670 8.513 2.952 0.940 26.851 11.576

0.410

NC

0.437

0.237

0.014

1.088

0.233

2 0.357

0.902

0.027

2 0.077

0.966

2.2 £ 1024 1.000 1.331 4.2 £ 1025 2.155 0.941 3.4 £ 1025 18.656 24.664 2.8 £ 1025 1.619 0.762

0.025 1.160 0.408 2 0.031 2 11.685 2 1.785 0.073 3.302 0.629 0.053 4.857 0.755

2 0.087 2 1.932 2 1.397 2 0.002 2 0.202 2 0.065 2 0.131 2 4.188 2 0.779 2 0.092 2 2.637 2 1.290

0.332 0.495 0.629 0.908 17.755 7.447 0.705 25.570 7.800 0.638 3.093 1.282

2 0.018

2 1.057

0.313

2 0.031

NC

0.876

0.008

2 0.288

0.713

0.007

2 0.389

0.645

S. Hoti and M. McAleer

Political

23

412

Table 6.7. Univariate GARCH(1,1) and GJR(1,1) estimates for Sub-Saharan Africa by risk return

Burkina Faso

Economic

Financial

Political

Cameroon

Economic

Financial

Political

Composite

Congo

Economic

Financial

0.038 0.818 0.490 2.064 3.477 1.764 2 0.015 2 0.651 2 0.134 2 0.027 2 0.731 2 3.842

0.471 0.813 0.326 0.164 2.315 2.441 0.630 0.663 3.096 2 0.181 2 0.376 2 0.056

2 0.694

0.509

2 0.987

2.228

NC

0.615

NC

2 0.208

1.9 £ 1024 4.117 1.263 1.8 £ 1024 2.588 1.319 1.4 £ 1024 1.623 67.327 6.0 £ 1025 3.530 1.944

0.134 4.469 1.158 0.200 1.850 2.002 2 0.029 2 4.874 2 2.280 0.286 2.948 2.102

0.760 15.516 6.689 0.450 2.146 1.990 0.558 1.967 4.385 0.545 5.349 3.712

2 0.174

0.894

2 0.610

0.650

NC

0.528

2 0.343

0.831

1.4 £ 1024 9.213 0.938 2.3 £ 1023 18.517 2.708

0.105 8.377 0.842 0.256 4.263 1.161

0.894 95.162 13.521 2 0.108 2 3.629 2 1.024

2 0.044

0.999

NC

0.148

3.0 £ 1024 13.270 1.939 6.0 £ 1024 4.145 16.941 1.3 £ 1024 3.784 1.567 3.9 £ 1025 2.617 10.843

2 0.072 2 2.294 2 6.211 2 0.019 2 0.261 2 0.406 2 0.016 2 1.550 2 1.951 0.023 1.931 0.851

0.240 2.931 2.521 2.577 5.930 1.215 2 0.091 2 5.738 2 5.134 2 0.097 2 3.680 2 2.157

0.661 73.258 3.721 0.212 4.045 2.636 0.781 12.351 3.456 0.906 23.879 34.707

3.4 £ 1024 8.036 70.392 1.8 £ 1024 2.351 1.244 2.1 £ 1024 7.936 54.158 6.8 £ 1025 5.115 117.552

2 0.036 2 4.255 2 1.287 0.169 1.690 0.967 2 0.041 2 1.506 2 1.967 2 0.048 2 6.563 2 2.914

0.363 4.064 0.979 0.039 0.335 0.150 0.149 1.559 1.263 0.661 4.098 1.983

0.666 12.749 8.480 0.456 2.023 1.841 0.404 4.992 3.347 0.542 6.933 7.211

1.4 £ 1024 11.235 793.364 3.8 £ 1023 7.908 3.840

2 0.045 2 12.928 2 2.009 0.229 2.316 1.035

0.242 9.293 2.272 0.078 30.503 0.472

0.909 85.909 23.912 2 0.129 2 1.121 2 1.015

0.049

NC

0.710

1.269

NC

1.482

2 0.062

NC

0.719

2 0.026

2 0.082

0.880

0.145

NC

0.811

0.189

2 0.617

0.644

0.033

NC

0.438

0.282

NC

0.824

0.077

NC

0.985

0.268

NC

0.139

413

(continued)

Univariate and Multivariate Estimates

Composite

3.2 £ 1024 0.903 0.337 4.7 £ 1024 4.814 1.851 1.8 £ 1024 0.390 17.414 3.7 £ 1024 2.603 0.365

414

Table 6.7. Continued Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Political

Composite

Economic

Financial

Political

Composite

Democratic Republic of Congo

Economic

Financial

6.5 £ 10 22.797 2.705 2.8 £ 1024 2.237 1.560

0.665 10.064 0.751 0.170 1.509 1.370

2 0.060 2 4.740 2 0.773 0.366 1.311 1.463

3.9 £ 1024 1.403 1.499 3.0 £ 1025 2.912 0.691 4.9 £ 1025 7.604 2.558 1.9 £ 1024 11.237 36.479

2 0.020 2 1.476 2 0.902 0.073 2.618 1.572 2.115 6.374 1.675 1.401 11.835 0.986

8.0 £ 1023 1.881 0.795 9.7 £ 1024 5.588 1.484

2 0.003 2 0.186 2 0.259 0.388 2.510 2.830

a

g

b

a þ g/2

Second

NC

0.605

20.815

0.537

0.888 9.458 12.182 0.880 22.190 8.122 0.318 11.531 2.725 2 0.049 2 1.409 2 1.784

20.142

0.868

20.073

0.953

20.436

2.433

NC

1.352

0.464 1.630 0.834 0.554 7.496 3.948

20.776

0.461

20.368

0.942

24

Moment Log

Second

0.224

NC

0.443

1.269

2 1.201

1.558

NC

0.894

0.192

2 0.424

0.828

1.966

2 0.466

2.313

6.0 £ 10 8.360 1.819 1.7 £ 1024 4.630 1.971

2 0.084 2 2.389 2 4.056 0.014 0.526 0.250

0.617 9.873 0.300 2.509 3.902 1.632

0.219 2.544 0.480 0.289 4.214 2.865

2.5 £ 1024 16.556 2.975 1.1 £ 1024 2.414 1.023 4.6 £ 1025 5.856 2.662 1.9 £ 1024 8.462 3.436

2 0.104 2 5.429 2 3.895 0.024 0.494 0.287 1.381 3.818 2.221 0.064 0.919 0.397

0.095 3.510 4.372 0.337 1.749 1.700 1.170 2.781 0.402 1.850 7.544 0.442

0.950 73.963 32.311 0.636 4.213 3.368 0.347 9.577 2.898 2 0.002 2 0.027 2 0.176

2 0.056

0.989

NC

0.987

1.6 £ 1023 17.783 3.457 9.7 £ 1024 5.727 1.474

2 0.094 2 5.809 2 1.698 0.349 2.274 1.311

0.141 4.990 1.624 0.091 0.744 0.211

0.936 71.516 26.852 0.553 7.657 3.949

2 0.024

NC

0.912

2 0.384

0.947

0.395

S. Hoti and M. McAleer

Coˆte d’Ivoire

24

v

Political

Composite

Ethiopia

Economic

Political

Composite

Gabon

Economic

Financial

Political

Composite

1.681 8.219 1.504 0.132 3.646 1.739

0.003 0.082 0.136 0.807 15.639 11.494

2 2.623

1.684

2 0.102

0.939

6.3 £ 1024 4.252 1.457 3.7 £ 1023 2.167 2.313 3.0 £ 1025 3.968 0.980 4.1 £ 1025 2.828 1.960

0.180 4.220 1.171 0.089 1.701 0.904 0.229 5.446 2.728 0.239 3.576 3.042

0.526 5.652 2.067 2 0.203 2 0.368 2 1.026 0.836 47.935 24.807 0.737 14.750 11.183

2 0.465

0.706

9.7 £ 1025 1.607 0.750 1.2 £ 1024 3.400 1.414 2.4 £ 1025 5.397 1.470 4.8 £ 1025 0.626 0.331

0.015 1.727 0.432 0.276 2.619 1.904 0.184 4.473 1.595 0.013 0.518 0.281

0.909 15.950 8.865 0.340 1.790 1.664 0.750 22.912 5.753 0.700 1.469 0.794

NC

2 0.114

2 0.048

1.065

2 0.114

0.977

2 0.081

0.924

2 0.818

0.616

2 0.174

0.934

2 0.339

0.713

1.5 £ 1023 18.689 1.771 6.7 £ 1025 2.760 1.548

0.007 0.201 0.362 0.010 0.804 0.152

3.946 5.366 1.519 0.171 3.988 1.528

0.140 4.118 1.237 0.896 43.831 17.317

1.979

2 1.934

2.119

0.096

2 0.099

0.992

6.3 £ 1024 4.738 2.858 1.2 £ 1023 1.666 1.880 3.3 £ 1025 6.566 2.245 5.1 £ 1025 3.741 2.436

2 0.031 2 1.095 2 3.509 2 0.068 2 3.682 2 2.578 2 0.055 2 6.309 2 3.070 0.053 0.923 0.999

0.284 2.713 1.528 0.079 6.672 3.075 0.411 6.823 2.492 0.473 3.330 2.040

0.544 6.193 3.821 0.555 2.038 1.534 0.902 83.661 22.659 0.709 16.048 9.608

0.111

NC

0.655

2 0.028

NC

0.527

0.151

NC

1.053

0.290

2 0.284

0.999

7.3 £ 1024 2.315 0.444 6.9 £ 1025 4.559 2.112 2.1 £ 1025 4.856 1.330 2.6 £ 1025 1.551 0.653

2 0.035 2 2.297 2 0.574 2 0.073 2 3.514 2 1.783 0.166 3.909 0.881 2 0.031 2 1.375 2 0.889

0.009 0.307 0.149 0.633 2.809 3.254 0.080 1.212 0.186 0.088 1.536 0.943

0.458 1.874 0.361 0.606 7.035 5.584 0.758 24.605 5.754 0.837 7.782 3.360

2 0.030

NC

0.428

0.243

NC

0.849

0.206

2 0.169

0.964

0.013

2 0.222

0.850

415

(continued)

Univariate and Multivariate Estimates

Financial

1.8 £ 1023 16.693 1.829 1.7 £ 1024 2.857 1.332

416

Table 6.7. Continued Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Ghana

Economic

Financial

Composite

Guinea

Economic

Financial

Political

Composite

g

b

a þ g/2

Second

4.8 £ 10 2.216 1.515 2.3 £ 1025 4.076 0.703 2.9 £ 1025 7.487 24.493 5.7 £ 1025 2.649 1.339

0.137 2.677 1.362 0.112 4.573 1.428 2 0.031 2 12.467 2 0.855 0.186 2.725 1.959

0.630 4.714 3.138 0.871 32.266 9.478 0.923 73.077 17.834 0.633 5.894 3.362

2 0.327

0.766

2 0.053

0.983

NC

0.891

2 0.258

0.819

9.5 £ 1025 4.654 1.538 3.2 £ 1024 1.267 1.349 1.2 £ 1025 3.750 1.231 3.2 £ 1024 8.327 504.900

0.193 4.726 1.605 2 0.023 2 1.762 2 2.915 0.233 4.199 2.436 0.094 4.452 2.416

0.759 23.717 7.507 0.684 2.526 2.550 0.793 20.731 12.275 2 0.691 2 5.220 2 3.578

2 0.140

0.952

NC

0.661

2 0.092

1.026

NC

a

2 0.597

24

4.8 £ 10 1.847 1.049 2.2 £ 1026 11.741 9.378 6.7 £ 1024 11.047 4.657 5.8 £ 1025 2.552 1.326

0.040 0.829 0.831 2 0.066 2 26.178 2 2.696 2 0.035 2 7.524 2 5.373 0.180 1.770 2.125

0.164 2.512 0.740 0.179 19.220 5.071 0.059 2.867 1.071 0.014 0.141 0.089

0.646 3.978 2.119 1.002 206.261 36.880 2 0.802 2 5.984 2 5.068 0.630 5.503 3.309

1.3 £ 1024 4.216 1.599 3.0 £ 1024 0.899 34.334 2.5 £ 1025 12.030 68.981 1.5 £ 1025 3.999 18.011

0.008 0.436 0.196 2 0.007 2 7.841 2 0.454 2 0.069 2 8.306 2 1.855 2 0.060 2 9.326 2 2.238

0.961 2.860 2.469 2 0.048 2 0.916 2 1.004 0.556 6.362 2.317 0.145 4.351 2.469

0.649 10.026 6.976 0.722 2.221 7.595 0.785 35.794 11.573 0.934 41.958 46.519

Moment Log

Second

0.122

2 0.387

0.768

0.024

NC

1.026

2 0.005

NC

20.807

0.187

2 0.266

0.817

0.488

2 0.420

1.138

2 0.030

2 0.334

0.691

0.209

NC

0.995

0.012

NC

0.946

S. Hoti and M. McAleer

Political

24

v

Kenya

Economic

Financial

Political

Liberia

Economic

Financial

Political

Composite

Malawi

Economic

Financial

0.038 1.923 0.874 0.057 61.548 5.152 20.034 29.632 21.124 20.059 21.946 24.466

0.862 14.402 6.174 2 0.882 2 12.830 2 9.657 0.907 54.846 21.057 0.772 56.463 4.508

2 0.110

2.2 £ 1023 0.230 0.030 2.3 £ 1023 0.546 1.114 3.0 £ 1023 3.137 1.164 7.6 £ 1025 3.025 1.196

20.002 20.185 20.046 20.009 20.560 21.796 0.439 1.537 0.515 0.014 3.250 0.615

2.1 £ 1023 13.153 3.627 1.3 £ 1025 2.165 0.493

0.276 100.441 1.371 0.066 3.903 1.859

0.900

NC

2 0.825

NC

0.874

NC

0.713

0.724 0.596 0.080 0.590 0.783 1.070 0.555 3.943 7.778 0.960 69.581 39.222

2 0.327

0.721

NC

0.581

2 0.457

0.994

2 0.028

0.974

2 0.234 2 5.224 2 1.314 0.940 48.430 21.743

NC

0.042

2 0.018

1.006

3.9 £ 1024 3.055 82.425 8.0 £ 1024 7.000 2.472 2.2 £ 1024 0.801 17.413 1.2 £ 1024 1.473 122.884

0.112 2.111 1.535 0.163 1.979 1.701 2 0.027 2 1.073 2 0.605 2 0.063 2 2.288 2 3.431

2 0.224 2 3.341 2 3.054 2 0.056 2 0.635 2 1.155 2 0.021 2 0.472 2 0.385 0.018 0.657 0.405

0.652 5.717 7.242 2 0.461 2 2.407 2 1.327 0.571 1.022 5.290 0.575 1.837 6.481

1.3 £ 1024 3.131 0.613 2.9 £ 1023 0.547 0.905 5.9 £ 1023 1.437 2.331 4.4 £ 1026 59.615 0.253

2 0.025 2 7.987 2 1.483 0.003 0.101 0.222 2 0.015 2 0.746 2 1.035 2 0.024 2 28.016 2 1.175

0.024 8.329 0.843 2 0.053 2 0.751 2 1.744 0.299 0.923 0.421 0.041 15.075 0.991

7.5 £ 1024 5.853 4.279 3.8 £ 1025 10.633 136.645

2 0.062 2 50.683 2 3.491 2 0.032 2 21.947 2 1.519

0.723 4.022 1.759 0.343 7.594 2.111

0.000

2 0.312

0.135

NC

2 0.326

2 0.037

NC

0.534

2 0.054

NC

0.521

0.995 288.299 39.145 0.489 0.526 0.643 0.606 2.204 4.452 0.999 995.617 37.063

2 0.013

2 0.041

0.982

2 0.023

2 0.711

0.466

NC

0.740

2 0.034

0.995

0.405 4.564 3.433 0.895 70.495 23.308

0.299

NC

0.705

0.139

NC

1.034

0.135

2 0.003

0.652

417

(continued)

Univariate and Multivariate Estimates

Composite

1.0 £ 1024 2.010 0.821 1.3 £ 1023 13.666 5.657 6.5 £ 1025 7.154 43.488 8.9 £ 1025 7.187 1.854

418

Table 6.7. Continued Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Political

Composite

Economic

Financial

Political

Composite

Mozambique

Economic

Financial

a

g

b

a þ g/2

Second

2.0 £ 10 7.368 45.775 1.5 £ 1024 1.724 60.157

20.019 28.876 23.582 20.036 21.474 20.492

0.958 148.132 47.548 0.510 1.828 4.258

NC

0.939

NC

0.474

4.6 £ 1025 16.152 27.199 5.8 £ 1024 0.318 1.152 1.1 £ 1024 1.728 11.473 1.2 £ 1025 6.121 15.508

20.021 2 13.509 21.120 20.009 20.269 21.686 20.011 24.586 20.206 20.024 2 11.679 24.130

0.997 581.353 55.157 0.635 0.553 1.239 0.845 8.961 12.196 0.986 116.589 89.529

NC

0.976

NC

0.626

NC

0.834

2 0.050

0.961

7.3 £ 1025 5.937 1.102 5.9 £ 1026 7.853 5.709

0.246 7.089 2.144 20.021 2 171.878 20.526

0.841 58.843 17.831 1.030 44.522 20.487

2 0.030

1.088

2 0.001

1.009

24

Moment Log

Second

1.2 £ 10 4.479 0.777 1.2 £ 1024 8.614 70.657

0.075 1.513 0.591 2 0.032 2 1.321 2 0.616

2 0.203 2 3.208 2 0.767 0.074 1.319 0.400

0.757 12.588 2.291 0.560 11.051 4.294

2 0.027

NC

0.730

0.005

NC

0.565

1.1 £ 1023 0.712 1.550 7.3 £ 1024 1.755 77.615 2.6 £ 1024 0.386 3.185 5.3 £ 1026 4.221 8.166

0.028 0.358 0.469 2 0.009 2 1.151 2 1.530 2 0.011 2 0.464 2 0.106 2 0.023 2 15.890 2 1.082

2 0.054 2 0.598 2 0.981 2 0.145 2 0.889 2 0.739 0.023 0.628 0.074 0.045 4.600 0.603

0.558 0.892 1.750 0.578 2.419 2.410 0.628 0.645 3.185 0.997 25.852 6.637

0.001

2 0.555

0.559

2 0.081

NC

0.497

0.001

NC

0.628

2 0.001

2 0.046

0.996

6.5 £ 1025 3.687 1.060 6.9 £ 1024 2.571 1.933

0.127 3.570 1.358 2 0.019 2 0.771 2 2.844

0.187 2.852 0.699 2 0.088 2 3.505 2 3.672

0.862 58.673 21.605 0.584 3.419 2.201

0.220

2 0.058

1.082

NC

0.521

2 0.063

S. Hoti and M. McAleer

Mali

25

v

Political

Composite

Nigeria

Economic

Political

Composite

Senegal

Economic

Financial

Political

Composite

0.048 3.117 2.100 2 0.029 2 16.285 2 1.282

0.933 81.214 19.547 1.010 26.422 30.685

2 0.031

0.980

2 0.044

0.981

1.7 £ 1023 1.264 0.786 1.3 £ 1024 4.397 1.199 8.5 £ 1025 2.158 1.435 2.9 £ 1024 2.669 1.369

0.051 1.163 0.569 0.114 7.066 1.013 0.108 3.165 1.055 0.159 2.321 1.392

0.456 1.099 0.706 0.848 36.213 8.147 0.720 6.704 4.611 0.449 2.317 1.533

2 0.704

0.508

2 0.076

0.962

2 0.215

0.828

2 0.600

0.609

1.5 £ 1024 8.961 0.758 1.5 £ 1024 4.082 1.334 2.5 £ 1025 3.375 3.308 7.4 £ 1025 0.909 41.908

0.036 3.229 0.522 0.318 2.196 2.133 2 0.029 2 6.348 2 1.320 2 0.025 2 4.258 2 0.645

0.778 34.779 3.131 0.248 1.361 1.175 0.903 26.334 21.805 0.538 1.000 3.947

2 0.214

0.814

2 1.132

0.566

NC

0.874

NC

0.513

1.0 £ 1025 5.696 0.637 4.2 £ 1025 3.941 11.761

2 0.002 2 0.445 2 0.066 2 0.029 2 6.030 2 0.531

0.089 2.752 1.712 0.422 5.441 1.336

0.953 70.125 14.545 0.786 19.064 13.276

0.043

2 0.051

0.995

0.182

NC

0.969

9.3 £ 1024 1.177 0.997 2.7 £ 1024 4.364 1.408 4.8 £ 1025 2.175 1.347 3.2 £ 1024 4.866 2.244

0.014 0.579 0.173 0.053 2.580 0.593 0.020 0.930 0.390 0.017 0.366 0.303

0.101 1.095 0.843 0.174 3.587 0.789 0.123 1.746 1.652 0.640 2.627 1.840

0.680 2.559 2.682 0.767 18.557 5.547 0.823 11.695 9.504 0.302 2.365 1.561

0.065

NC

0.744

0.140

2 0.211

0.907

0.082

2 0.173

0.904

0.337

2 1.150

0.639

7.9 £ 1024 1.308 263.483 1.3 £ 1024 3.581 1.103 1.6 £ 1025 3.578 20.259 7.7 £ 1025 0.703 52.723

2 0.027 2 0.245 2 0.501 0.064 0.747 1.292 2 0.029 2 7.077 2 1.545 2 0.007 2 0.071 2 0.109

0.009 0.078 0.150 0.434 1.688 1.611 0.028 2.654 0.739 2 0.019 2 0.197 2 0.380

0.534 1.397 3.891 0.359 2.035 1.216 0.934 44.445 28.648 0.534 0.772 4.036

2 0.023

NC

0.511

2 0.937

0.641

2 0.015

NC

0.919

2 0.016

2 0.641

0.517

0.281

419

(continued)

Univariate and Multivariate Estimates

Financial

1.2 £ 1025 4.134 1.148 1.4 £ 1025 8.100 12.263

420

Table 6.7. Continued Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Sierra Leone

Economic

Financial

Composite

South Africa

Economic

Financial

Political

Composite

a

g

b

a þ g/2

Second

8.6 £ 10 6.013 1.391 3.6 £ 1024 3.082 0.569 4.3 £ 10206 2.064 0.439 3.0 £ 1025 2.206 0.881

0.309 6.160 1.706 0.144 2.625 1.679 0.226 11.238 1.418 0.282 8.278 1.135

0.729 29.047 7.684 0.847 17.689 6.667 0.874 247.468 12.603 0.791 46.688 6.107

20.157

1.037

20.099

0.991

20.014

1.100

20.046

1.073

1.2 £ 1024 0.397 0.510 8.3 £ 1024 12.867 5.021 2.7 £ 10206 7.523 2.234 1.6 £ 1024 8.685 2.286

0.001 0.058 0.025 0.357 7.085 1.984 2 0.015 2 19.595 2 0.576 0.215 3.030 1.171

0.753 1.204 1.515 2 0.178 2 2.044 2 2.744 1.008 112.144 30.992 2 0.035 2 0.328 2 0.181

20.283

0.754

NC

0.179

20.019

0.993

NC

0.180

23

1.3 £ 10 3.742 1.590 5.5 £ 1024 3.931 0.751 3.4 £ 10206 3.061 0.650 1.2 £ 1025 1.788 0.726 1.1 £ 1025 69.033 16.020 6.7 £ 1024 12.231 4.195 2.1 £ 1024 1.483 32.303 1.5 £ 1024 9.743 2.597

Moment Log

Second

0.081 2.433 0.727 0.066 2.169 0.727 0.174 8.832 1.207 0.011 0.558 0.255

2.814 4.518 1.315 0.377 2.451 1.266 0.011 0.400 0.052 0.918 6.658 1.586

0.416 20.549 2.767 0.764 13.153 7.336 0.895 194.812 15.009 0.784 56.078 9.235

1.488

2 0.771

1.904

0.254

2 0.226

1.018

0.179

2 0.001

1.074

0.470

2 0.230

1.254

2 0.070 2 10.400 2 1.592 0.269 2.744 1.374 0.005 0.371 0.068 0.277 2.629 1.077

0.089 9.548 2.233 0.275 1.443 1.203 2 0.175 2 2.649 2 1.948 2 0.120 2 0.655 2 0.404

1.007 140.869 22.045 2 0.120 2 2.346 2 1.810 0.649 2.479 5.490 2 0.020 2 0.218 2 0.141

2 0.026

NC

0.981

0.407

NC

0.286

2 0.427

0.567

NC

0.197

2 0.082

0.217

S. Hoti and M. McAleer

Political

24

v

Sudan

Economic

Financial

Political

Tanzania

Economic

Financial

Political

Composite

Togo

Economic

Financial

0.074 2.737 1.035 0.200 1.629 1.727 0.347 3.360 1.706 0.071 1.387 1.064

0.779 7.826 5.777 0.523 3.337 1.448 0.592 7.797 5.430 0.568 2.373 1.018

2 0.183

0.853

2 0.521

0.723

2 0.278

0.939

2 0.477

0.639

1.6 £ 1024 6.735 1.116 5.0 £ 1025 7.340 0.641 3.2 £ 1025 2.311 13.840 2.3 £ 1025 1.564 1.204

0.193 4.819 1.896 0.130 3.717 0.991 2 0.023 2 6.098 2 0.788 2 0.014 2 3.214 2 1.537

0.833 34.890 18.773 0.873 47.595 7.631 0.861 12.519 21.951 0.938 18.713 16.744

2 0.077

1.026

2 0.067

1.002

NC

0.838

2 0.082

0.924

1.2 £ 1024 4.728 1.121 4.5 £ 1025 3.490 0.708

0.110 4.455 1.753 0.059 2.498 1.169

0.823 27.343 12.467 0.781 13.044 3.866

2 0.104

0.933

2 0.195

0.840

1.1 £ 1023 3.359 1.878 2.2 £ 1023 3.349 78.025 7.2 £ 1024 4.107 1.647 5.1 £ 1024 5.219 2.034

2 0.032 2 5.693 2 1.827 2 0.018 2 3.068 2 0.380 0.261 2.137 0.917 0.009 0.135 0.271

0.264 4.313 1.388 0.369 1.492 0.752 0.139 0.614 0.545 1.206 4.356 1.582

0.787 14.326 9.272 0.547 4.149 3.890 0.605 7.536 5.186 0.481 9.633 2.571

2.1 £ 1024 3.868 0.641 2.1 £ 1024 17.990 255.638 1.2 £ 1024 1.575 2.166 1.3 £ 1025 1.952 1.353

2 0.026 2 4.828 2 0.663 2 0.108 2 14.406 2 1.427 2 0.038 2 3.335 2 3.065 2 0.026 2 8.110 2 1.472

0.194 5.413 1.269 0.328 9.024 1.451 0.284 1.695 1.017 0.084 3.616 0.852

1.6 £ 1023 2.103 1.096 6.0 £ 1025 3.151 4.803

0.102 2.375 0.675 2 0.032 2 2.439 2 0.918

2 0.153 2 9.962 2 1.043 0.158 2.729 1.318

0.100

2 0.299

0.886

0.166

NC

0.713

0.330

2 0.303

0.935

0.612

2 0.715

1.093

0.895 30.580 7.200 0.808 60.471 9.194 0.523 1.717 2.568 0.953 30.940 28.137

0.071

NC

0.966

0.056

NC

0.864

0.103

NC

0.626

0.015

2 0.083

0.969

0.397 1.442 0.747 0.843 17.968 16.957

0.025

2 0.806

0.422

0.047

NC

0.891

421

(continued)

Univariate and Multivariate Estimates

Composite

1.3 £ 1023 1.795 1.400 1.3 £ 1023 3.046 0.780 7.2 £ 1024 4.456 1.741 8.7 £ 1024 1.787 0.646

422

Table 6.7. Continued Country

Risk Returns

GARCH(1,1)

v

a

GJR(1,1)

b

Moment Log

Political

Uganda

Economic

Financial

Political

Composite

Zambia

Economic

3.9 £ 10 2.163 442.531 1.4 £ 1025 0.855 0.477

2 0.015 2 5.297 2 1.207 0.021 0.989 1.043

0.597 3.171 3.810 0.920 10.538 7.496

4.2 £ 1025 4.202 6.354 3.4 £ 1024 3.818 0.361 5.3 £ 1024 1.730 0.532 1.8 £ 1025 1.743 0.810

0.019 147.059 0.998 0.017 1.303 0.206 0.054 0.729 1.069 0.053 3.127 1.598

7.6 £ 1024 2.933 1.375

0.099 2.948 1.302

a

g

b

a þ g/2

Second

NC

0.581

2 0.062

0.941

0.970 73.453 72.371 0.715 10.394 0.939 0.177 0.373 0.123 0.933 44.460 23.449

2 0.007

0.990

2 0.314

0.732

2 1.557

0.231

2 0.024

0.985

0.720 8.465 5.394

2 0.236

0.819

24

Moment Log

Second

1.9 £ 10 0.816 1.348 9.6 £ 1025 1.965 0.964

0.008 0.916 0.412 0.030 0.658 0.480

2 0.033 2 2.077 2 1.656 2 0.076 2 1.053 2 1.217

0.821 3.619 6.640 0.733 5.465 2.516

2 0.008

2 0.192

0.813

2 0.008

2 0.283

0.725

2.6 £ 1025 6.389 57.100 9.0 £ 1024 127.797 0.658 3.2 £ 1026 2.356 1.083 1.2 £ 1026 0.404 0.143

0.015 2.903 0.576 0.050 1.547 0.350 2 0.023 2 5.248 2 0.467 0.005 0.853 0.169

0.154 11.005 1.123 2 0.101 2 3.609 2 0.700 0.062 5.244 1.979 0.152 5.060 2.195

0.939 345.745 49.092 0.370 13.282 0.374 0.996 567.498 20.335 0.944 125.202 47.769

0.092

2 0.048

1.032

2 0.001

2 0.918

0.369

0.008

2 0.035

1.005

0.081

2 0.052

1.025

1.0 £ 1023 6.474 2.499

2 0.058 2 6.123 2 4.685

0.522 4.341 1.818

0.636 13.180 6.131

0.202

NC

0.838

S. Hoti and M. McAleer

Composite

24

v

Financial

Political

Composite

Economic

Financial

Political

Composite

0.184 3.934 1.889 2 0.009 2 13.017 2 0.536 2 0.026 2 2.310 2 1.986

0.729 15.106 4.741 0.983 20.330 10.644 0.818 3.815 6.308

2 0.180

0.913

2 0.033

0.974

NC

0.792

1.4 £ 1024 3.831 1.565 1.1 £ 1025 1.610 0.267 2.7 £ 1024 1.856 1.458 1.9 £ 1025 2.323 0.743

0.225 4.709 1.726 0.064 5.004 1.794 0.110 2.443 0.771 0.092 2.762 1.613

0.737 15.004 7.632 0.950 85.614 16.213 0.554 2.508 2.500 0.866 18.614 8.104

2 0.129

0.962

2 0.005

1.014

2 0.481

0.664

2 0.061

0.958

1.4 £ 1024 79.616 39.130 3.8 £ 1024 2.474 2.090 1.1 £ 1024 4.820 12.595

2 0.069 2 6.350 2 1.523 0.005 0.155 0.429 2 0.026 2 6.547 2 3.027

0.408 8.667 3.021 2 0.229 2 0.655 2 2.671 2 0.056 2 2.034 2 0.930

0.847 81.083 20.149 0.630 4.273 3.947 0.882 35.972 23.621

1.3 £ 1024 3.278 1.953 4.3 £ 1025 5.705 1.024 4.7 £ 1024 1.359 1.856 4.4 £ 1026 1.571 0.805

0.030 1.197 0.249 0.021 1.909 0.329 2 0.030 2 1.486 2 4.207 2 0.033 2 5.026 2 0.811

0.302 4.309 1.464 0.755 4.407 3.138 0.219 1.715 0.898 0.100 6.744 2.481

0.764 14.782 10.680 0.800 33.582 9.190 0.574 1.832 2.727 0.983 120.981 64.768

0.135

NC

0.982

2 0.109

2 0.457

0.521

2 0.054

NC

0.828

0.181

2 0.235

0.945

0.399

2 0.200

1.199

0.080

NC

0.653

0.017

2 0.052

1.000

Notes: (1) NC denotes that the log-moment could not be calculated because [(a þ gI(ht))h2t þ b] in (7) or (ah2t þ b) in (8) was negative for one observation. (2) The three entries for each parameter are their respective estimate, the asymptotic t-ratio and the Bollerslev and Wooldridge (1992) robust t-ratio.

Univariate and Multivariate Estimates

Zimbabwe

3.1 £ 1024 4.762 1.172 2.0 £ 1025 28.116 0.434 1.6 £ 1024 0.897 1.558

423

Country

Risk Returns

GARCH(1,1)

v

a

b

GJR(1,1) Moment Log

Austria

Economic

Financial

Composite

Belgium

Economic

Financial

Political

Composite

v

a

g

Second

8.4 £ 10 3.004 0.538 1.4 £ 1024 0.582 2.259 7.6 £ 1025 9.203 4.578 2.4 £ 1025 6.153 3.485

0.060 2.284 1.939 20.014 20.480 20.146 0.906 7.329 1.789 0.776 6.060 2.299

0.907 2 0.049 34.175 9.441 0.455 NC 0.488 2.612 20.015 NC 20.232 21.418 0.145 2 0.890 1.874 1.633

0.967

1.7 £ 1025 2.637 2.537 2.7 £ 1024 4.099 10.743 2.1 £ 1024 14.091 13.789 1.7 £ 1026 0.546 0.473

20.045 0.939 2 0.122 23.215 33.951 22.797 21.863 20.012 0.573 NC 20.513 5.465 20.068 2.263 0.093 20.818 NC 11.953 2 18.885 5.414 2 14.101 0.023 0.960 2 0.021 1.009 15.831 0.887 16.981

0.895

0.441

0.891

0.921

0.562

20.725

0.983

b

a þ g/2

Moment Log

26

7.8 £ 10 6.249 1.304 3.7 £ 1027 21.326 0.166 9.1 £ 1025 8.387 5.840 3.3 £ 1025 6.586 9.144

0.144 20.192 5.067 27.249 1.896 22.118 20.026 0.027 23.685 3.700 20.553 0.913 1.219 21.156 5.663 24.947 1.216 21.155 1.287 21.303 17.976 210.553 1.818 21.835

1.2 £ 1024 1.078 0.886 2.6 £ 1024 0.601 7.481 4.2 £ 1025 0.357 0.193 7.0 £ 1027 0.035 0.116

20.031 20.561 20.495 20.072 21.259 20.486 0.006 0.154 0.076 0.006 0.197 0.102

20.022 20.516 20.368 0.061 1.121 0.242 0.020 0.414 0.201 20.005 20.163 20.064

Second

0.923 0.048 0.024 62.151 14.853 1.005 20.013 NC 485.253 30.708 20.014 0.641 NC 20.206 21.130 0.065 0.636 20.956 0.903 0.671

0.971

0.382 20.042 NC 0.627 0.523 0.575 20.042 NC 0.813 2.195 0.641 0.016 20.436 0.654 0.354 0.989 0.003 20.006 3.643 11.330

0.341

0.992

0.627

0.701

0.533

0.657

0.992

S. Hoti and M. McAleer

Political

26

424

Table 6.8. Univariate GARCH(1,1) and GJR(1,1) estimates for West Europe by risk return

Cyprus

Economic

Financial

Political

Denmark

Economic

Financial

Political

Composite

Finland

Economic

Financial

0.113 5.512 1.608 20.025 2 11.140 21.290 20.022 21.509 20.954 20.043 2 10.968 22.302

0.884 2 0.032 56.754 14.447 0.970 2 0.073 126.298 29.388 0.209 NC 0.265 0.128 0.990 NC 172.547 56.432

0.997

1.8 £ 1025 1.542 0.955 3.3 £ 1025 1.452 0.353 2.3 £ 1025 1.791 73.071 3.7 £ 1026 1.705 2.447

0.066 2.004 1.222 20.008 21.561 21.032 20.071 2 12.109 24.576 20.016 22.274 20.637

0.828 2 0.122 9.420 5.554 0.931 2 0.083 19.230 3.844 0.790 NC 5.511 17.773 0.957 2 0.064 34.384 17.524

0.894

0.147 0.810 2 0.080 3.171 14.562 2.265 14.104 20.001 1.029 0.028 20.683 1349.625 20.027 33.061

0.957

2.5 £ 1025 2.567 1.452 21.7 £ 1026 235.641 22.986

0.945

0.187

0.948

0.922

0.718

0.941

1.028

5.7 £ 1025 1439.141 3.171 2.0 £ 1024 0.954 2.147 1.3 £ 1024 2.171 18.034 6.4 £ 1025 0.750 5.417

20.047 23.454 25.353 20.024 20.721 21.981 20.011 20.395 20.427 20.024 20.573 20.525

0.372 2.912 2.414 0.079 0.695 1.109 20.096 23.806 21.294 0.056 0.641 0.559

0.819 0.139 NC 29.572 14.980 0.638 0.015 NC 1.679 2.867 0.572 20.059 20.577 2.684 5.209 0.550 0.004 NC 0.916 5.471

0.958

1.3 £ 1025 1.312 1.047 6.6 £ 1024 2.937 2.191 1.7 £ 1025 5.686 22.763 7.0 £ 1025 0.406 1.718

0.005 0.222 0.146 0.191 1.403 0.544 20.084 23.267 22.173 0.057 0.425 0.685

0.125 0.860 0.067 20.146 1.662 10.385 1.404 8.330 20.197 20.039 0.093 NC 20.667 20.112 20.563 20.190 0.025 0.860 20.072 NC 0.701 44.855 0.670 25.020 20.084 0.321 0.015 21.064 21.305 0.194 21.010 0.732

0.927

3.9 £ 1025 2.891 2.907 1.3 £ 1024 1.877 1.166

20.045 25.869 23.150 0.002 0.038 0.018

0.348 3.259 2.694 0.271 1.311 1.828

0.810 12.576 13.905 0.519 2.021 4.420

0.129

NC

0.653

0.513

0.554

0.053

0.789

0.337

Univariate and Multivariate Estimates

Composite

7.9 £ 1026 2.372 1.029 1.9 £ 1025 5.324 5.529 2.0 £ 1024 1.023 0.498 6.9 £ 1026 6.270 26.228

0.939

0.138 20.652

0.657

425

(continued)

426

Table 6.8. Continued Country

Risk Returns

GARCH(1,1)

v

a

b

GJR(1,1) Moment Log

Political

Composite

Economic

Financial

Political

Composite

Germany

Economic

Financial

a

g

b

a þ g/2

Second

4.8 £ 10 3.943 1.287 1.0 £ 1026 0.782 0.608

0.173 5.567 1.724 0.031 1.562 1.098

0.788 2 0.101 23.927 7.621 0.954 2 0.018 25.533 17.603

0.961

3.7 £ 1025 2.472 1.643 2.6 £ 1024 0.420 1.180 1.1 £ 1025 3.352 0.682 1.5 £ 1024 4.235 3.591

0.173 2.917 1.984 20.014 22.083 22.489 0.073 2.521 1.233 0.177 1.634 1.488

0.638 2 0.274 6.072 3.606 0.551 NC 0.512 1.093 0.878 2 0.068 28.898 7.277 20.338 NC 21.267 21.340

0.810

9.0 £ 1024 11.193 4.846 1.4 £ 1025 2.462 1.009

0.345 3.756 0.531 20.015 27.702 20.767

20.092 NC 27.736 21.117 0.988 2 0.030 123.966 25.921

0.985

0.537

0.951

20.161

0.253

0.973

Moment Log

26

6.0 £ 10 4.403 1.336 9.1 £ 1027 0.684 1.216

0.284 4.978 1.479 0.032 1.386 1.272

2.3 £ 1025 2.505 1.230 1.8 £ 1024 0.608 1.079 3.1 £ 1025 3.087 2.890 1.4 £ 1024 2.547 2.289

0.036 0.896 0.564 20.046 21.841 20.347 20.010 20.483 20.394 0.159 1.520 0.692

8.8 £ 1024 3.865 4.391 3.1 £ 1024 16.372 15.226

0.387 5.400 0.379 0.000 0.004 0.010

20.133 21.919 20.709 20.061 21.811 20.844

0.741 19.722 6.784 0.983 27.417 40.143

Second

0.217 20.102

0.958

0.002

0.984

0.011

0.304 0.722 0.188 20.281 0.910 2.143 9.768 2.572 4.749 0.033 0.651 20.030 NC 0.621 0.671 1.117 0.250 1.570 0.606 0.654 0.293 20.441 0.947 5.039 10.022 1.493 7.110 20.172 20.357 0.072 NC 2 0.284 22.223 20.704 20.775 20.724 20.185 20.091 0.295 NC 20.632 20.330 20.182 20.833 20.017 0.566 20.008 20.569 20.256 19.268 20.076 2.683

0.204

0.557

S. Hoti and M. McAleer

France

26

v

Political

Composite

Greece

Economic

Political

Composite

Iceland

Economic

Financial

Political

Composite

0.929 2 0.051 16.934 3.929 20.042 NC 21.705 25.777

0.952

5.3 £ 1024 5.734 5.157 6.7 £ 1024 8.468 2.520 2.1 £ 1025 2.368 2.226 2.1 £ 1024 2.785 1.202

0.524 6.247 1.583 0.178 2.068 1.450 20.034 23.499 21.771 0.058 0.752 0.969

20.037 NC 20.353 20.950 20.128 NC 21.020 21.292 0.946 2 0.096 26.010 22.625 20.341 NC 20.785 20.344

0.486

9.4 £ 1025 2.337 0.645 2.1 £ 1025 3.587 0.624 2.6 £ 1025 0.905 18.759 2.5 £ 1025 2.904 1.264

0.047 2.138 0.827 0.117 3.101 1.616 20.026 21.595 20.742 0.155 2.077 1.572

0.795 2 0.182 9.837 2.737 0.818 2 0.131 17.273 4.632 0.554 NC 1.104 3.903 0.345 2 0.805 1.723 0.803

1.212

0.050

0.913

20.282

0.842

0.935

0.528

0.501

4.3 £ 1026 1.744 0.757 1.3 £ 1024 4.419 6.671

0.006 0.436 0.186 0.939 5.029 0.355

0.078 1.905 1.093 20.987 25.159 20.371

5.7 £ 1024 7.919 5.351 7.5 £ 1026 13.202 11.218 2.6 £ 1025 3.722 37.447 7.4 £ 1026 3.365 1.302

0.191 1.831 1.508 20.061 213.132 21.061 20.076 28.570 26.022 20.062 24.656 22.094

8.4 £ 1025 2.592 0.593 6.9 £ 1025 2.828 5.992 2.6 £ 1025 1.439 7.130 2.2 £ 1025 2.865 1.023

0.042 2.118 0.589 20.055 21.601 21.064 20.026 21.778 20.253 0.178 1.702 1.303

0.932 39.040 13.781 0.025 0.147 0.151

0.045 20.065

0.976

0.445 21.988

0.470

0.638 3.472 0.943 0.074 16.994 2.526 0.090 4.054 3.058 0.155 4.054 3.033

20.072 0.510 NC 21.073 21.187 1.021 20.024 NC 252.692 20.914 0.938 20.031 NC 40.623 40.087 0.957 0.016 20.121 46.632 18.267

0.438

0.019 0.506 0.190 0.228 2.082 1.655 20.017 21.276 20.175 20.075 20.564 20.551

0.810 0.051 20.168 12.126 2.837 0.777 0.059 NC 10.016 15.605 0.543 20.034 NC 1.668 3.118 0.429 0.140 20.602 2.374 0.917

0.997

0.907

0.973

0.861

0.837

0.509

0.569

(continued)

427

0.023 1.292 0.470 1.254 12.284 1.106

Univariate and Multivariate Estimates

Financial

6.6 £ 1026 1.107 0.240 6.4 £ 1025 8.644 5.326

428

Table 6.8. Continued Country

Risk Returns

GARCH(1,1)

v

a

b

GJR(1,1) Moment Log

Ireland

Economic

Financial

Composite

Italy

Economic

Financial

Political

Composite

1.0 £ 10 2.664 1.042 6.4 £ 1025 2.712 0.966 3.9 £ 1027 0.408 0.431 3.4 £ 1025 3.113 1.435

0.177 1.562 1.430 0.205 2.016 2.337 20.008 22.231 20.739 0.182 2.677 1.410

5.1 £ 1024 8.537 3.897 4.1 £ 1025 3.579 1.303 1.0 £ 1026 0.262 0.081 1.4 £ 1026 0.602 0.275

0.102 2.597 1.941 0.157 3.437 1.916 0.021 1.325 0.933 0.032 1.727 1.322

0.377 1.666 0.870 0.517 2.948 1.974 1.004 220.532 85.611 0.297 1.539 0.749

a

g

b

a þ g/2

Second

2 0.741

0.554

2 0.493

0.722

2 0.004

0.996

2 0.882

0.479

20.690 NC 24.525 23.264 0.730 2 0.196 11.272 5.920 0.980 2 0.002 33.771 13.681 0.959 2 0.012 25.905 14.989

20.588

0.887

1.001

0.991

Moment Log

24

1.2 £ 10 2.934 1.577 4.5 £ 1025 2.466 262.802 2.9 £ 1025 2.234 1.763 3.4 £ 1025 2.906 1.485 5.2 £ 1024 12.505 2.963 5.2 £ 1025 5.413 1.604 1.1 £ 1025 2.596 0.606 21.4 £ 1026 23.038 24.219

0.315 1.453 1.179 20.084 2 235.656 23.050 20.069 22.732 22.570 0.199 1.716 1.423

20.191 20.836 20.692 0.366 2.881 3.531 0.144 2.552 2.546 20.032 20.219 20.170

0.284 1.247 0.982 0.781 8.552 11.809 0.790 8.234 5.376 0.296 1.433 0.770

0.048 0.912 1.153 20.016 20.609 20.617 20.004 20.306 20.178 20.026 23.235 20.792

0.095 1.281 1.010 0.612 3.336 2.403 0.196 3.887 1.468 0.111 6.182 2.568

20.552 27.072 21.463 0.623 9.293 6.728 0.886 36.483 7.797 1.000 137.449 60.954

Second

0.220 20.850

0.504

0.099

NC

0.880

0.003

NC

0.793

0.183 20.865

0.479

0.095

NC

2 0.457

0.290

NC

0.914

0.094 20.126

0.979

0.030 20.027

1.029

S. Hoti and M. McAleer

Political

24

v

Luxembourg

Economic

Financial

Political

Malta

Economic

Financial

Political

Composite

Netherlands

Economic

Financial

0.553 20.173 NC 1.980 2 49.341 1.859 24.071 0.025 0.964 2 0.019 1.481 22.080 0.738 6.844 20.019 0.831 2 0.221 22.822 9.319 20.680 2.810 0.339 20.173 NC 4.548 24.321 2.522 23.135

0.380

0.989

0.812

0.167

6.0 £ 1025 3.360 1.925 1.9 £ 1025 3.308 1.924 2.2 £ 1025 2.818 1.170 2.7 £ 1026 5.278 0.440

1.617 5.864 1.750 20.015 2 18.865 20.177 0.032 2.641 0.447 20.013 2 18.639 20.239

0.369 2 0.334 6.205 3.207 0.977 NC 78.877 10.390 0.907 2 0.077 25.876 26.898 1.006 2 0.026 234.950 10.398

1.986

4.7 £ 1025 3.610 1.836 2.9 £ 1024 1.843 1.038

0.217 3.986 1.161 0.161 1.032 1.127

0.473 2 0.509 4.414 1.861 0.354 2 0.927 1.014 1.552

0.691

0.962

0.939

0.993

0.516

1.3 £ 1024 2.153 1.198 23.2 £ 1027 22.345 22.114 1.9 £ 1025 1.267 1.563 2.9 £ 1025 11.853 61.277

20.008 20.240 20.172 20.044 211.864 22.002 0.063 0.847 0.576 0.338 2.514 2.973

0.179 1.190 1.128 0.048 71.780 10.121 20.094 21.615 20.845 0.082 0.551 0.510

0.612 0.082 20.509 3.444 2.431 1.023 20.020 NC 181.472 43.984 0.544 0.016 20.560 1.521 1.931 20.150 0.379 NC 23.106 23.185

0.694

5.3 £ 1025 6.354 2.556 2.6 £ 1024 0.793 2.774 1.7 £ 1024 1.387 374.529 9.0 £ 1025 1.050 5.548

4.208 5.128 1.806 20.013 20.478 20.081 20.016 21.895 21.427 20.013 20.594 21.713

24.160 25.135 21.797 20.015 20.314 20.102 20.054 20.263 20.130 20.024 22.376 20.048

0.412 2.128 20.074 14.702 3.175 0.536 20.020 NC 0.913 3.560 0.623 20.043 NC 2.202 3.291 0.570 20.025 NC 1.364 1.854

2.540

4.0 £ 1025 4.483 1.359 2.3 £ 1024 1.243 1.920

0.019 0.497 0.252 0.115 3.413 0.517

0.419 3.290 1.467 20.124 24.334 20.565

0.522 6.342 1.808 0.732 3.406 4.697

1.003

0.561

0.228

0.516

0.580

0.545

0.229 20.617

0.751

0.053 20.267

0.785

429

(continued)

Univariate and Multivariate Estimates

Composite

4.4 £ 1024 15.382 3.549 3.9 £ 1026 0.801 0.186 4.8 £ 1026 2.012 0.608 2.9 £ 1025 12.838 68.420

430

Table 6.8. Continued Country

Risk Returns

GARCH(1,1)

v

a

b

GJR(1,1) Moment Log

Political

Composite

Economic

Financial

Political

Composite

Portugal

Economic

Financial

a

g

Second

2.9 £ 10 2.544 0.946 9.4 £ 1026 4.450 2.562

0.016 1.507 0.407 0.675 4.234 2.052

0.905 2 0.083 28.201 9.123 0.398 2 0.402 4.347 4.148

0.921

2.7 £ 1026 0.679 0.215 2.0 £ 1026 7.101 0.710 7.9 £ 1025 2.923 0.931 2.4 £ 1025 1.857 1.042

0.027 1.400 1.401 0.099 7.825 0.876 0.145 2.069 0.820 0.103 1.420 1.183

0.964 25.983 9.901 0.898 85.264 7.873 0.414 2.091 1.119 0.378 1.230 0.730

2 0.014

0.991

2 0.044

0.997

2 0.724

0.559

2 0.802

0.481

9.5 £ 1026 0.903 0.306 1.2 £ 1024 2.717 18.256

0.013 1.111 0.609 20.017 26.044 20.284

0.955 2 0.032 21.605 8.861 0.827 NC 12.602 8.969

0.968

1.073

0.810

b

a þ g/2

Moment Log

26

1.7 £ 10 3.847 3.264 3.9 £ 1027 2.848 0.651

0.041 2.389 1.374 0.215 5.897 1.618

20.086 25.440 22.754 20.224 25.928 21.532

0.951 20.002 20.013 48.348 33.000 0.924 0.103 0.066 61.969 43.661

7.3 £ 1025 0.803 1.126 2.7 £ 1026 8.868 0.727 7.8 £ 1025 2.240 0.808 3.5 £ 1025 6.273 5.374

20.004 20.345 20.146 0.023 1.776 0.306 0.098 1.713 0.413 0.667 3.428 1.465

20.073 22.397 22.114 0.455 5.515 0.919 0.065 0.806 0.165 20.646 23.500 21.396

0.683 20.041 20.386 1.570 2.721 0.844 0.250 20.149 58.454 5.294 0.428 0.130 20.729 1.690 0.893 0.025 0.344 21.840 0.240 0.423

3.4 £ 1026 2.138 0.334 3.0 £ 1024 0.479 6.875

0.001 0.137 0.017 0.004 0.054 0.032

20.012 21.631 20.104 20.020 20.490 20.220

0.984 20.005 20.015 98.813 18.388 0.558 20.007 20.578 0.604 4.066

Second 0.949

1.027

0.642

1.094

0.559

0.369

0.979

0.552

S. Hoti and M. McAleer

Norway

26

v

Political

Composite

Spain

Economic

Political

Composite

Sweden

Economic

Financial

Political

Composite

20.041 21.354 24.299 0.268 3.957 1.095

5.9 £ 1025 1.528 1.479 5.7 £ 1025 2.995 0.981 1.7 £ 1024 5.693 2.944 6.2 £ 1025 3.229 1.661

0.061 1.477 0.856 0.143 2.319 1.948 0.210 3.125 1.367 0.164 2.494 1.294

0.791 6.072 7.788 0.638 5.527 2.596 0.024 0.170 0.130 0.254 1.319 0.648

2 0.176

0.852

2 0.327

0.782

2 2.896

0.234

2 1.066

0.418

6.7 £ 1026 15.084 17.422 2.7 £ 1024 2.052 1.324 4.4 £ 1026 4.725 4.276 4.4 £ 1025 7.924 1.715

20.032 2 11.473 22.229 0.125 1.465 0.769 20.030 24.809 21.197 0.314 3.511 1.443

1.015 74.557 70.529 0.384 1.316 1.485 0.976 156.163 25.759 0.156 2.499 0.389

2 0.035

0.983

2 0.837

0.509

2 0.062

0.945

2 1.278

0.470

0.575 NC 1.926 1.153 0.441 2 0.522 5.022 1.101

0.534

0.708

2.8 £ 1025 2.791 49.817 3.5 £ 1025 4.133 1.291

20.051 26.564 22.582 0.233 3.628 0.634

4.8 £ 1025 7.074 22.096 3.7 £ 1025 6.214 223.650 2.0 £ 1024 6.432 217.673 5.9 £ 1025 3.002 1.509

20.036 28.887 20.971 20.069 216.472 23.452 0.257 3.272 1.232 0.183 2.042 1.185

2.9 £ 1025 2.575 1.407 4.7 £ 1024 4.144 2.998 6.0 £ 1025 0.940 0.850 4.4 £ 1025 7.718 1.654

20.062 25.192 21.782 20.222 21.447 23.985 0.060 0.805 0.668 0.212 2.486 0.701

0.121 3.444 1.261 0.162 0.927 0.499

0.863 15.230 27.579 0.439 3.957 1.260

0.009

0.314 20.549

0.753

0.235 0.829 5.059 59.360 1.129 17.192 0.285 0.809 5.008 25.767 3.294 14.506 20.092 20.108 20.507 20.804 20.371 20.719 20.078 0.296 20.634 1.500 20.403 0.726

0.081

NC

0.911

0.074

NC

0.883

0.211

NC

0.104

0.079 3.417 1.609 0.529 1.740 3.320 20.069 20.695 20.769 0.187 1.072 0.462

NC

0.872

0.145 20.919

0.441

0.938 20.022 NC 29.932 20.119 0.415 0.042 NC 2.921 2.968 0.229 0.025 21.323 0.289 0.248 0.166 0.305 21.343 2.118 0.399

0.916

0.457

Univariate and Multivariate Estimates

Financial

9.8 £ 1025 1.492 0.879 3.7 £ 1025 4.482 1.215

0.254

0.472

431

(continued)

432

Table 6.8. Continued Country

Risk Returns

GARCH(1,1)

v

Economic

Financial

Political

Composite

Turkey

Economic

Financial

b

Moment Log

Second

1.9 £ 1024 6.094 1.422 9.4 £ 1027 0.974 20.853 7.9 £ 1027 8.010 2.471 4.7 £ 1027 1.153 0.472

0.219 1.781 1.250 0.846 7.411 1.233 20.016 2 17.982 20.604 0.037 2.519 1.548

0.090 0.705 0.182 0.674 30.846 7.657 1.011 37.211 26.827 0.958 44.097 20.399

2 1.891

0.309

2 0.200

1.521

2 0.014

0.994

2 0.012

0.995

7.8 £ 1027 0.079 0.008 2.8 £ 1023 15.872 2.744

0.092 4.682 1.366 0.632 10.022 0.458

0.947 85.494 17.095 20.047 22.041 20.420

0.009

1.039

NC

0.585

v

a

g

b

a þ g/2

Moment Log

6.9 £ 1025 4.128 0.662 27.1 £ 1027 25.965 211.709 5.2 £ 1025 0.965 1.863 4.3 £ 1026 4.445 6.327 3.5 £ 1026 0.308 0.031 1.7 £ 1023 12.225 1.803

0.029 0.139 0.635 1.397 0.122 0.453 20.024 0.569 24.425 7.554 21.003 1.350 0.054 20.097 0.826 230.736 0.563 21.068 0.166 20.242 6.860 27.354 2.393 23.705 0.049 1.586 0.450 2.350 5.638 1.152

0.067 1.652 0.277 22.228 24.934 21.094

0.643 6.945 1.407 0.835 55.702 8.117 0.458 0.810 1.481 0.862 26.022 17.171 0.953 67.641 18.976 0.013 0.250 0.260

Second

0.099 20.407

0.742

0.260

1.096

NC

0.005 20.734

0.463

0.045 20.040

0.908

0.082 20.011

1.035

1.236 22.252

1.249

S. Hoti and M. McAleer

Switzerland

a

GJR(1,1)

Political

Composite

United Kingdom Economic

Political

Composite

0.285 4.808 2.016 1.363 8.686 2.379

0.779 2 0.054 20.333 13.521 0.081 2 0.935 2.733 0.904

1.064

8.9 £ 1025 6.230 1.942 2.1 £ 1024 0.494 6.094 2.4 £ 1024 6.505 44.956 3.0 £ 1025 0.816 0.712

0.433 6.379 0.802 20.009 21.000 20.044 0.140 2.557 0.961 20.030 21.279 21.491

0.349 2 0.631 5.596 1.104 0.568 NC 0.650 1.682 20.220 NC 22.174 21.356 0.668 2 0.465 1.544 1.319

0.782

1.443

0.559

20.080

0.638

1.1 £ 1025 1.310 1.292 2.6 £ 1024 8.951 2.691

0.082 3.491 0.938 1.572 6.058 1.770

0.328 4.562 1.498 20.488 21.190 20.457

6.2 £ 1025 5.483 1.612 2.1 £ 1024 0.837 2.647 8.2 £ 1025 1.633 34.289 3.3 £ 1025 1.322 35.329

0.032 0.995 0.528 0.021 0.081 0.053 20.036 22.338 20.578 20.049 24.741 21.040

0.726 6.280 0.984 20.030 20.117 20.086 0.250 3.868 0.859 0.018 0.991 0.315

0.834 29.168 15.774 0.086 2.731 0.966

0.246 20.109

1.080

1.329 20.834

1.415

0.495 0.395 20.649 8.029 1.914 0.574 0.006 20.542 1.125 3.078 0.525 0.089 NC 1.925 4.600 0.631 20.040 NC 2.065 7.722

0.890

0.580

0.614

0.592

Notes: (1) NC denotes that the log-moment could not be calculated because [(a þ gI(ht))h2t þ b] in (7) or (ah2t þ b) in (8) was negative for one observation. (2) The three entries for each parameter are their respective estimate, the asymptotic t-ratio and the Bollerslev and Wooldridge (1992) robust t-ratio.

Univariate and Multivariate Estimates

Financial

3.0 £ 1025 2.602 1.507 2.5 £ 1024 9.628 2.660

433

434

S. Hoti and M. McAleer

Table 6.9. Summary of univariate GARCH(1,1) and GJR(1,1) estimates by region Regions

Model

Conditions

a . 0 b . 0 g – 0 a þ g=2 . 0

Log Second Number of Moment Moment Equations

Central and South Asia

GARCH GJR

13

14 15

6

13

12 8

13 11

16 16

East Asian and the Paciﬁc

GARCH GJR

46

58 65

18

42

51 31

55 56

68 68

East Europe

GARCH GJR

25

31 34

8

26

19 18

29 30

36 36

Middle East and North Africa

GARCH GJR

50

56 64

11

57

41 37

56 57

72 72

North and GARCH Central America GJR

36

48 54

7

42

48 34

52 54

60 60

South America

GARCH GJR

36

40 37

6

37

38 28

37 35

40 40

Sub-Saharan Africa

GARCH GJR

70

91 96

27

69

74 53

84 82

104 104

West Europe

GARCH GJR

56

66 72

19

61

58 44

70 72

84 84

Total

GARCH GJR

332

404 437

102

347

341 253

396 397

480 480

Table 6.10. Preferred model for countries by risk return Regions

Countries

Risk Return Economic

Financial

Political

Composite

Central and South Asia

Bangladesh India Pakistan Sri Lanka

GARCH X GARCH GARCH

GARCH GARCH GJR GARCH

GARCH GJR X X

GJR GJR GARCH X

East Asia and the Paciﬁc

Australia Brunei China Hong Kong Indonesia Japan Malaysia Mongolia New Zealand

GARCH GJR X GARCH X X GARCH X GARCH

GARCH* GARCH GARCH GARCH GARCH GJR X GARCH GARCH*

GJR* X GARCH X GARCH GJR* GARCH GJR* GJR

GARCH GARCH GJR* GARCH GARCH GARCH X GARCH GARCH

(continued)

Univariate and Multivariate Estimates

Table 6.10. Regions

435

Continued

Countries

Risk Return Economic

Financial

Political

Composite

North Korea Papua New Guinea Philippines Singapore South Korea Taiwan Thailand Vietnam

X X

X GARCH

X GARCH

X GARCH

GARCH GJR GARCH GARCH GARCH GARCH

GARCH GARCH GJR* GARCH GARCH* X

X X GARCH GJR* GJR* X

GJR* GJR GARCH GJR* X X

East Europe

Albania Bulgaria Czech Republic Hungary Poland Romania Russia Slovakia Yugoslavia

GARCH GJR* X GARCH GARCH GJR X GARCH GARCH

GJR GARCH X GJR* GJR* GARCH GARCH GARCH* GJR*

GARCH X GJR X GJR* GARCH X X GJR*

GARCH GJR GARCH X X GARCH GARCH GJR* GARCH

Middle East and North Africa

Algeria Bahrain Egypt Iran Iraq Israel Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syria Tunisia United Arab Emirates Yemen

GARCH GARCH GARCH GJR GJR* GARCH GARCH GARCH X GARCH GARCH GJR* GJR* GARCH GARCH X GARCH

GARCH X GARCH X GJR* GARCH X GARCH GARCH X X X X GARCH GARCH X GARCH

GJR X GJR GARCH GARCH GARCH GJR* GARCH GJR GJR* GJR* GJR* X GJR GJR GARCH GARCH

GARCH GJR* GARCH X X GARCH X GARCH GARCH GJR* GJR* X X GARCH GJR GARCH GJR*

GJR*

GJR*

X

GJR*

Bahamas Canada Costa Rica

GARCH GARCH GARCH

GJR* X GARCH

GJR* X X

X X GARCH

North and Central America

(continued)

436

S. Hoti and M. McAleer

Table 6.10. Regions

Continued

Countries

Risk Return Economic

Financial

Political

Composite

Cuba Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Trinidad and Tobago USA

GJR* X

GARCH GJR*

X X

X X

GJR* GJR GJR GJR* GJR* GARCH GJR GARCH GJR*

GARCH GJR* GJR* X GARCH X GJR* X GARCH

GARCH X X GARCH* X GARCH GARCH GARCH X

GJR X GARCH GARCH GJR* X GJR GARCH GARCH

GARCH

GARCH

GARCH

GARCH

South America

Argentina Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela

GARCH GARCH GARCH GARCH GARCH X GARCH GJR GJR* GJR

GARCH GARCH GARCH GARCH GARCH GARCH GARCH GARCH GJR* GARCH

GJR GARCH GARCH GJR* GARCH GARCH GARCH GARCH GARCH GARCH

GARCH GARCH* GARCH GARCH GJR GJR GARCH GARCH GARCH GARCH

Sub-Saharan Africa

Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR of Congo Ethiopia Gabon Ghana Guinea Kenya Liberia Malawi

GARCH X GJR GARCH GJR X X GARCH GARCH GARCH GJR GARCH* X GJR*

GARCH X X GARCH X GARCH GARCH X GJR GARCH X X X GARCH*

GARCH GARCH X GJR* GJR* X X GARCH* GARCH X GJR X GARCH X

GARCH GJR* X GJR GARCH X GARCH GJR GARCH GARCH GJR X GARCH GJR*

(continued)

Univariate and Multivariate Estimates

Table 6.10. Regions

West Europe

437

Continued

Countries

Risk Return Economic

Financial

Political

Composite

Mali Mozambique Nigeria Senegal Sierra Leone South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe

GJR* GARCH GARCH GARCH GARCH GARCH* GARCH GARCH GARCH GARCH GARCH GARCH

X X GARCH GARCH GARCH X GARCH GARCH GARCH GARCH GJR GJR

GJR* GARCH GARCH X GARCH X GARCH GJR* X GJR X GARCH

X GJR* GJR X GARCH X GARCH GJR* GARCH GJR X GJR

Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal Spain Sweden Switzerland Turkey UK

GARCH* X GJR GARCH GJR GJR X X GARCH GARCH X GJR* X GARCH GARCH* GARCH GARCH X GARCH GJR* GARCH

X X GJR* X GJR X X X GARCH GJR GJR GARCH* X GARCH GARCH X GJR GJR GARCH X GJR*

X GJR* X X GARCH GARCH GARCH X X GJR GARCH GJR* GARCH GARCH* GARCH GJR* GARCH GJR* GJR* GARCH GJR*

GJR GARCH GJR* GJR* GARCH X GJR* GJR GARCH GARCH GARCH* X X GARCH GARCH GARCH GARCH GARCH GJR X X

Notes: (1) GARCH* refers to cases when the estimate g for a particular risk return was signiﬁcant, but the GARCH(1,1) estimates were superior to their GJR(1,1) counterparts. (2) GJR* refers to cases when the g estimate for a particular risk return was insigniﬁcant, but the GJR(1,1) estimates were superior to their GARCH(1,1) counterparts. (3) X refers to cases when neither model was suitable.

438

S. Hoti and M. McAleer

Table 6.11. GARCH(1,1) static conditional correlations for Central and South Asia by risk return Countries Economic risk returns Bangladesh India Pakistan Sri Lanka Financial risk returns Bangladesh India Pakistan Sri Lanka Political risk returns Bangladesh India Pakistan Sri Lanka

Bangladesh

India

Pakistan

Sri Lanka

1.000

0.092 1.000

20.033 0.258 1.000

0.220 0.100 2 0.084 1.000

1.000

0.259 1.000

20.050 0.089 1.000

0.173 0.048 0.186 1.000

1.000

0.160 1.000

0.150 0.210 1.000

0.025 0.245 0.223 1.000

0.093 1.000

0.209 0.328 1.000

0.129 0.239 0.196 1.000

Composite risk returns Bangladesh 1.000 India Pakistan Sri Lanka

Table 6.12. GARCH(1,1) static conditional correlations for East Asia and the Paciﬁc by risk return Countries

Australia

Financial risk returns Australia 1.000 Brunei China Hong Kong Indonesia

0.137 1.000

China

Hong Kong

0.039 0.109 0.064 0.267 1.000 20.093 1.000

2 0.326 2 0.334 0.350 1.000 0.308 20.163 1.000 0.069 1.000

Indonesia

Japan

North Korea

Malaysia Mongolia

New Papua Philippines Singapore Zealand New Guinea

South Korea

Taiwan

Thailand Vietnam

2 0.062 0.193 0.061 0.370

0.189 0.177 0.158 0.137

0.121 0.180 0.003 0.120

0.176 0.050 0.088 0.235

20.084 20.088 0.132 20.055

0.254 0.195 20.063 0.231

0.131 0.010 2 0.001 0.068

0.112 0.188 0.098 0.125

0.044 0.064 2 0.090 0.111

20.115 0.136 0.032 0.279 0.189 20.070 20.126 0.165

0.110 0.033 0.017 0.122

20.005 0.211 0.051 0.099

1.000

0.209 1.000

0.349 0.208 1.000

0.339 0.431 0.166

20.139 0.182 20.158

0.260 0.059 20.025

2 0.093 0.059 2 0.025

0.365 0.304 0.082

0.025 0.033 2 0.159

0.087 0.201 0.290 0.306 0.166 20.070

0.091 0.303 0.026

0.266 0.114 0.262

1.000

0.125 1.000

20.002 20.208 1.000

2 0.008 0.101 2 0.077

0.270 20.028 0.172

0.031 2 0.105 0.171

0.282 0.305 0.272 20.056 20.270 0.302

0.326 0.157 0.026

0.149 0.079 0.023

1.000

20.006 1.000

2 0.187 0.203 1.000

0.013 0.180 20.194 1.000

0.032 0.196 0.231 0.038

0.022 0.164 0.100 0.223

0.006 0.133 20.033 0.184

1.000

0.135 1.000

0.079 0.135 1.000

0.122 0.474 20.032 20.349 20.064 20.373 0.083 0.274

0.596 2 0.475 2 0.424 0.338

0.071 0.065 0.015 20.048

0.455

0.072

0.514 2 0.236 2 0.297 0.201 1.000

0.315 0.342 2 0.203 20.230 2 0.162 20.298 0.146 0.241 0.287

0.216

0.528 20.253 20.361 0.289

0.282 20.307 20.255 0.205

0.608 20.341 20.377 0.248

2 0.301 0.113 0.120 2 0.132

0.339 20.234 20.018 0.404

0.483 2 0.120 2 0.261 0.335

0.439

0.252

0.518

2 0.078

0.455

0.405

0.014

0.328

439

(continued)

Univariate and Multivariate Estimates

Economic risk returns Australia 1.000 Brunei China Hong Kong Indonesia Japan North Korea Malaysia Mongolia New Zealand PNG Philippines Singapore South Korea Taiwan Thailand Vietnam

Brunei

440

Table 6.12. Countries

Australia

Brunei

China

Hong Kong

Indonesia

Political risk returns Australia 1.000 Brunei China Hong Kong Indonesia Japan North Korea Malaysia Mongolia

1.000

2 0.104 2 0.041 1.000 2 0.024 1.000

0.048 0.246 0.047 1.000

North Korea 0.075 1.000

Continued

Malaysia Mongolia

New Papua Philippines Singapore Zealand New Guinea

South Korea

Taiwan

Thailand Vietnam

0.335 0.342

0.036 0.328

0.403 0.241

0.030 2 0.073

0.199 0.190

0.318 0.277

0.074 0.043

0.433 0.351

0.474 0.385

20.032 0.142

1.000

0.316 1.000

0.471 0.288 1.000

2 0.215 2 0.157 2 0.142

0.344 0.230 0.415

0.526 0.262 0.512

0.092 0.037 0.036

0.465 0.330 0.539

0.553 0.376 0.606

0.024 0.012 0.022

1.000

20.071 1.000

2 0.166 0.368 1.000

20.048 20.050 0.058 0.387 0.061 0.541 1.000 0.033

2 0.136 0.406 0.542 0.091

0.086 20.035 0.019 20.125

1.000

0.599 1.000

20.030 20.063 1.000

0.084 0.102 2 0.045 0.018

0.103 20.137 2 0.060 20.060 0.020 0.096 0.096 20.087

20.027 0.118 20.094 0.134

0.108 20.116 20.177 0.033

0.137 20.027 0.033 0.178

0.012 2 0.032 0.044 0.159

0.057 20.018 20.067 0.265

2 0.008 0.022 2 0.148 0.245

0.069 0.055 0.053 20.127 0.082 0.119 0.136 0.129

0.129 2 0.079 2 0.048 0.098

0.007 20.046 20.020 0.046

1.000

0.173 20.075 1.000 20.045 1.000

0.196 0.171 20.005 1.000

0.062 20.001 20.019 0.161 1.000

0.003 0.036 20.149 0.002 0.076

0.017 0.082 2 0.016 0.052 0.225

20.003 0.281 0.033 0.148 0.062

0.178 0.154 0.007 0.341 0.205

20.028 20.139 0.051 0.116 20.137 20.054 0.047 0.091 20.059 0.190

0.104 0.139 2 0.105 0.150 0.175

0.005 20.055 0.003 0.005 0.136

S. Hoti and M. McAleer

Japan North Korea Malaysia Mongolia New Zealand PNG Philippines Singapore South Korea Taiwan Thailand Vietnam

Japan

New Zealand PNG Philippines Singapore South Korea Taiwan Thailand Vietnam 0.087 0.035 0.099 1.000 2 0.050 0.212 1.000 20.031 1.000

0.114 0.192 2 0.028 0.127 1.000

0.222 0.068 0.123 0.132 0.247 1.000

0.062 0.081 0.008 0.017 0.144 0.139 1.000

0.083

0.094

0.075

0.024

0.255

0.110

20.075

1.000

0.120 1.000

0.108 0.144 1.000

20.018 0.101 0.111 1.000

0.168 0.058 0.145 0.062

0.139 0.026 0.206 0.007

0.117 20.055 20.010 20.026

1.000

0.237 1.000

0.100 0.103 1.000

0.057 0.185 0.109 0.209 0.097 20.024 0.213 0.085 0.218 0.085 0.119 0.318 0.183 0.058

0.172 2 0.016 2 0.087 0.111 0.088 0.171 0.118

0.131 0.134 20.030 0.129 0.328 0.119 0.159

0.202 0.236 20.074 0.169 0.451 0.347 0.172

20.036 20.093 20.296 0.080 0.047 20.012 20.067

0.257 0.122 20.051 0.191 0.176 0.224 0.086

0.051 2 0.002 0.012 0.111 0.008 0.067 0.062

0.036 0.084 0.079 0.224 0.157 0.266 0.044

0.029 0.154 2 0.009 0.102 0.061 0.105 2 0.140

1.000

0.125 1.000

0.218 0.028 1.000

2 0.049 0.067 0.025

0.169 20.043 0.160

0.146 0.123 2 0.022

0.360 20.091 0.065

0.254 0.101 0.325

0.290 0.141 0.191

0.222 0.132 0.137

1.000

0.082 1.000

2 0.029 0.120 1.000

20.077 0.115 0.228 0.231 0.007 0.142 1.000 20.031

2 0.007 0.088 0.131 0.123

0.054 0.029 0.026 0.069

1.000

0.298 1.000

0.110 0.014 1.000

Univariate and Multivariate Estimates

Composite risk returns Australia 1.000 Brunei China Hong Kong Indonesia Japan North Korea Malaysia Mongolia New Zealand PNG Philippines Singapore South Korea Taiwan Thailand Vietnam

1.000

441

442

S. Hoti and M. McAleer

Table 6.13. GARCH(1,1) static conditional correlations for East Europe by risk return Countries

Albania

Economic risk returns Albania 1.000 Bulgaria Czech Republic Hungary Poland Romania Russia Slovakia Yugoslavia Financial risk returns Albania 1.000 Bulgaria Czech Republic Hungary Poland Romania Russia Slovakia Yugoslavia Political risk returns Albania 1.000 Bulgaria Czech Republic Hungary Poland Romania Russia Slovakia Yugoslavia Composite risk returns Albania 1.000 Bulgaria Czech Republic Hungary Poland Romania Russia Slovakia Yugoslavia

Bulgaria

Czech Republic

0.312 1.000

0.146 0.011 1.000

0.129 1.000

0.316 1.000

0.223 1.000

0.028 0.394 1.000

0.050 0.179 1.000

0.123 0.229 1.000

Hungary

Poland

Romania

Russia

Slovakia

Yugoslavia

0.212 0.031 0.141

0.221 0.093 0.475

0.081 0.126 0.212

2 0.021 2 0.076 0.392

0.207 0.048 0.160

0.119 0.166 2 0.195

1.000

0.406 1.000

2 0.068 0.038 1.000

0.027 0.135 0.299 1.000

0.084 0.182 0.313 0.212 1.000

2 0.121 2 0.218 0.196 2 0.041 0.076 1.000

2 0.059 0.414 0.737

0.153 0.254 0.623

0.101 0.484 0.529

2 0.125 2 0.314 2 0.148

2 0.153 0.133 0.443

2 0.037 0.129 0.307

1.000

0.519 1.000

0.425 0.448 1.000

2 0.048 2 0.111 2 0.391 1.000

0.620 0.389 0.156 0.183 1.000

0.177 0.073 0.209 2 0.085 0.085 1.000

0.255 0.229 0.230

0.156 0.264 0.454

0.080 0.239 0.347

0.086 2 0.004 0.117

2 0.106 0.050 0.250

0.243 0.170 2 0.070

1.000

0.328 1.000

0.223 0.269 1.000

0.092 0.042 2 0.009 1.000

0.137 0.360 0.075 2 0.016 1.000

0.230 0.061 0.093 0.155 2 0.099 1.000

0.175 0.211 0.305

0.224 0.253 0.453

0.044 0.298 0.137

2 0.069 2 0.077 0.158

0.013 0.117 0.145

0.153 2 0.027 2 0.014

1.000

0.511 1.000

0.095 0.234 1.000

0.077 0.025 0.004 1.000

0.191 0.265 0.304 0.094 1.000

0.092 2 0.011 2 0.059 0.185 2 0.058 1.000

Table 6.14. GARCH(1,1) static conditional correlations for Middle East and North Africa by risk return Countries

Algeria Bahrain

Financial risk returns Algeria 1.000 Bahrain Egypt Iran Iraq Israel Jordan Kuwait Lebanon Libya Morocco Oman

Kuwait

Lebanon

Libya

Morocco

0.322 2 0.148 0.011 0.048 20.034 0.120 2 0.051 2 0.059 20.012 0.330 0.114 0.107 0.202 0.083 0.439 1.000 2 0.143 2 0.078 0.206 0.283 1.000 0.077 20.141 20.044 1.000 20.080 0.045 1.000 0.226 1.000

0.002 0.212 0.217 0.084 2 0.067 0.230 0.207 0.351 1.000

2 0.061 0.138 0.320 0.039 0.196 0.186 2 0.012 0.268 0.200 1.000

20.182 0.116 0.103 20.016 20.302 20.024 0.322 0.248 0.169 0.169 1.000

0.115 20.030 0.270 0.358 0.120 0.250 0.213 0.257 0.074 20.105 0.064 20.060 0.122 0.106 0.448 0.479 0.544 0.277 0.379 0.178 0.255 0.167 1.000 0.332 1.000

0.000 0.134 0.249 0.250 0.165 0.038 0.354 0.414 0.331 0.405 0.021 0.519 0.213 1.000

0.297 0.319 0.185 0.065 0.240 0.117 0.087 2 0.048 2 0.082 2 0.001 20.080 0.032 1.000 0.227 0.032 0.279 0.367 0.262 1.000 2 0.075 0.241 0.343 0.294 1.000 2 0.023 0.041 20.251 1.000 0.303 0.176 1.000 0.198 1.000

2 0.049 0.041 0.061 0.175 2 0.147 0.397 0.254 0.222 1.000

0.034 0.020 0.421 0.076 2 0.162 0.443 0.300 0.173 0.214 1.000

0.266 20.015 0.174 0.336 0.100 0.433 0.414 0.264 0.319 0.062 1.000

0.153 0.307 20.085 20.119 0.264 0.201 0.492 0.123 0.054 0.219 0.480 0.129 0.432 0.159 0.311 0.133 0.380 20.189 0.268 0.031 0.685 0.431 1.000 0.418

0.152 0.118 0.383 0.525 20.062 0.272 0.326 0.360 0.280 0.419 0.257 0.597

0.081 2 0.097 1.000 0.246 1.000

20.158 1.000

Iran

Iraq

Israel

Jordan

Oman

Qatar

Saudi Arabia

Syria

Tunisia

UAE

Yemen

2 0.121 0.046 0.036 0.092 0.076 0.232 0.189 0.064 0.219 0.312 0.262 0.175 2 0.128 0.108 0.285 0.149 0.231 20.277 0.285 20.180 0.228 0.151 0.126 0.070 2 0.258 0.301 0.240 0.346 2 0.060 0.375 0.225 0.290 0.024 0.368 0.217 0.169 0.468 0.241 0.320 0.078 0.066 0.568 20.012 0.248 0.110 0.412 0.222 0.108 0.038 0.274 0.237 0.261 2 0.020 0.220 0.451 0.180 1.000 0.090 0.038 0.014 1.000 0.047 0.315 1.000 0.148 1.000 0.159 0.223 0.139 0.078 20.102 0.045 0.365 20.009 0.408 0.300 0.321 0.355 2 0.012 0.068 20.160 0.418 0.291 0.363 0.366 0.377 0.299 0.244 0.139 0.293 0.171 0.118 0.489 0.505 20.126 0.504 0.374 0.662 0.409 0.477 0.513 0.498

0.031 0.022 0.042 0.290 0.146 0.341 0.332 0.178 0.186 0.067 0.516 0.552

443

(continued)

Univariate and Multivariate Estimates

Economic risk returns Algeria 1.000 Bahrain Egypt Iran Iraq Israel Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syria Tunisia UAE Yemen

Egypt

444

Table 6.14. Continued Algeria Bahrain

Egypt

Iran

Iraq

Israel

Jordan

Kuwait

Lebanon

Libya

Morocco

Qatar Saudi Arabia Syria Tunisia UAE Yemen Political risk returns Algeria 1.000 Bahrain Egypt Iran Iraq Israel Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syria

20.056 1.000

0.032 2 0.068 2 0.100 2 0.023 20.053 20.066 0.262 0.404 0.261 0.373 0.373 0.626 1.000 0.132 0.328 0.257 0.331 0.306 1.000 0.347 0.213 0.365 0.376 1.000 0.256 0.405 0.302 1.000 0.437 0.266 1.000 0.425 1.000

0.067 0.308 0.147 0.313 0.186 0.302 0.225 0.211 1.000

2 0.251 0.394 0.062 0.390 0.262 0.146 0.157 0.296 0.194 1.000

20.150 0.490 0.268 0.456 0.509 0.463 0.577 0.381 0.336 0.368 1.000

Oman

Qatar

Saudi Arabia

1.000

0.251 1.000

20.176 20.004 0.340 0.414 0.234 0.376 0.313 0.302 0.296 0.372 0.190 0.500 0.360 0.476 0.335 0.481 0.258 0.336 0.272 0.277 0.562 0.655 1.000 0.528 1.000

0.154 0.432 0.221 0.347 0.254 0.319 0.414 0.561 0.278 0.264 0.327 0.277 0.531 1.000

Syria 0.235 0.488 1.000

Tunisia 0.285 0.124 0.222 1.000

2 0.094 20.131 0.501 0.228 0.195 0.222 0.329 0.267 0.273 0.345 0.266 0.341 0.358 0.346 0.488 0.088 0.075 0.190 0.448 0.175 0.332 0.614 0.232 0.278 0.329 0.476 0.382 0.172 1.000 0.118

UAE 0.286 0.624 0.306 0.127 1.000

Yemen 0.267 0.230 0.286 0.409 0.209 1.000

0.140 20.073 0.526 0.281 0.327 0.157 0.480 0.366 0.475 0.363 0.462 0.304 0.607 0.412 0.607 0.243 0.335 0.201 0.327 0.281 0.663 0.549 0.393 0.338 0.697 0.432 0.704 0.266 0.426 0.180

S. Hoti and M. McAleer

Countries

Tunisia UAE Yemen 0.032 1.000

0.038 0.444 1.000

0.192 0.421 0.378 1.000

0.007 2 0.140 20.066 20.016 0.146 0.170 0.230 0.504 0.386 0.272 0.364 0.530 0.171 0.121 0.303 0.421 1.000 0.243 0.104 0.227 1.000 0.360 0.327 1.000 0.334 1.000

0.002 0.300 0.383 0.329 0.204 0.411 0.304 0.415 1.000

0.075 0.259 0.274 0.392 0.324 0.209 0.140 0.327 0.295 1.000

20.241 0.396 0.343 0.375 0.333 0.507 0.421 0.445 0.457 0.385 1.000

0.058 0.385 0.399 0.428 0.398 0.301 0.205 0.460 0.424 0.367 0.540 1.000

0.022 0.382 0.345 0.334 0.277 0.461 0.213 0.513 0.332 0.273 0.488 0.465 1.000

0.115 0.339 0.457 0.431 0.347 0.326 0.374 0.646 0.389 0.378 0.385 0.476 0.440 1.000

0.438 1.000

0.487 0.441 1.000

2 0.034 20.161 20.004 20.059 0.342 0.362 0.322 0.219 0.339 0.332 0.414 0.234 0.344 0.447 0.388 0.288 0.291 0.305 0.369 0.225 0.369 0.442 0.426 0.336 0.310 0.383 0.397 0.244 0.483 0.378 0.545 0.244 0.289 0.433 0.441 0.167 0.529 0.331 0.515 0.197 0.353 0.802 0.544 0.488 0.329 0.603 0.369 0.313 0.287 0.430 0.503 0.391 0.433 0.354 0.615 0.317 1.000 0.323 0.429 0.122 1.000 0.462 0.487 1.000 0.365 1.000

Univariate and Multivariate Estimates

Composite risk returns Algeria 1.000 Bahrain Egypt Iran Iraq Israel Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syria Tunisia UAE Yemen

1.000

445

446

Table 6.15. GARCH(1,1) static conditional correlations for North and Central America by risk return Countries

Financial risk returns Bahamas Canada Costa Rica Cuba Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama

Canada

Costa Rica

1.000

2 0.026 1.000

0.064 0.082 1.000

1.000

2 0.101 1.000

0.046 0.124 1.000

Cuba

Dominican Republic

El Salvador

Guatemala

Haiti

Honduras

Jamaica

Mexico

Nicaragua

Panama

Trinidad and Tobago

USA

0.063 0.203 0.247 1.000

0.019 0.161 20.082 20.216 1.000

0.128 0.017 20.004 20.122 0.127 1.000

0.056 0.073 20.073 0.087 0.191 0.191 1.000

20.133 20.074 20.102 20.066 20.066 20.138 20.107 1.000

2 0.035 2 0.001 0.089 0.203 0.128 0.075 0.327 2 0.053 1.000

0.213 0.038 0.082 0.230 2 0.109 2 0.169 0.090 0.113 0.186 1.000

0.126 0.158 20.114 0.038 0.088 20.049 0.121 0.110 20.024 20.031 1.000

0.082 0.103 0.080 0.101 2 0.026 2 0.066 0.182 2 0.025 0.323 0.146 2 0.117 1.000

0.174 0.046 0.006 0.021 2 0.004 2 0.027 2 0.124 2 0.123 2 0.182 2 0.170 0.194 2 0.015 1.000

0.101 0.038 0.057 0.118 0.074 20.130 0.010 0.023 0.196 0.075 0.096 0.031 0.153 1.000

20.113 0.187 20.142 20.163 0.198 0.007 20.025 20.159 0.010 20.113 0.016 20.035 0.029 0.109 1.000

0.044 0.492 20.001 1.000

0.003 20.284 20.087 20.050 1.000

20.043 20.491 20.176 20.126 0.418 1.000

20.026 20.635 20.149 20.498 0.274 0.487 1.000

20.029 20.281 20.157 20.081 0.305 0.371 0.252 1.000

2 0.003 0.102 0.094 2 0.040 0.037 0.109 0.125 0.215 1.000

0.090 0.235 0.232 0.136 2 0.148 2 0.168 2 0.298 2 0.056 0.109 1.000

20.086 0.474 0.173 0.283 20.270 20.335 20.378 20.293 0.064 0.307 1.000

0.074 0.564 0.207 0.493 2 0.310 2 0.402 2 0.424 2 0.118 0.081 0.237 0.414 1.000

0.062 0.284 0.235 0.265 2 0.051 2 0.166 2 0.183 0.011 0.111 0.103 0.308 0.339 1.000

20.025 20.268 20.192 20.226 0.157 0.178 0.247 0.158 0.050 20.080 20.147 20.275 20.187

0.171 0.598 0.234 0.517 20.303 20.446 20.601 20.204 20.067 0.389 0.417 0.602 0.347

S. Hoti and M. McAleer

Economic risk returns Bahamas Canada Costa Rica Cuba Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Trinidad and Tobago USA

Bahamas

Trinidad and Tobago USA

Composite risk returns Bahamas Canada Costa Rica Cuba Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Trinidad and Tobago USA

20.256 1.000

1.000

0.211 1.000

0.118 0.004 1.000

0.129 0.092 20.024 1.000

0.316 0.122 0.179 0.133 1.000

0.233 0.059 0.125 0.002 0.200 1.000

0.107 0.170 0.213 0.117 0.105 0.222 1.000

0.028 20.053 0.082 0.032 0.315 20.001 0.078 1.000

0.159 0.052 0.259 2 0.019 0.151 0.259 0.259 0.135 1.000

0.141 0.014 0.181 2 0.087 0.236 0.168 0.143 0.104 0.329 1.000

0.122 0.177 0.093 0.065 0.069 0.112 0.100 0.021 0.111 0.066 1.000

0.142 0.193 0.267 0.072 0.213 0.322 0.151 0.080 0.247 0.222 0.136 1.000

0.087 0.141 0.214 2 0.125 0.181 0.160 0.063 0.159 0.128 0.176 0.089 0.170 1.000

0.314 0.141 20.075 20.039 0.231 0.154 0.078 20.029 0.045 0.228 0.112 0.140 0.113 1.000

0.060 0.216 20.070 0.076 0.149 0.021 0.102 0.055 0.003 20.036 0.055 0.037 0.012 0.168 1.000

1.000

0.124 1.000

0.077 0.030 1.000

0.155 0.135 0.102 1.000

0.173 0.152 0.017 0.079 1.000

0.092 0.053 0.020 0.111 0.280 1.000

0.024 0.013 0.056 20.007 0.284 0.351 1.000

20.042 20.170 20.052 0.039 0.261 0.034 0.096 1.000

0.138 0.100 0.129 0.186 0.277 0.231 0.345 0.070 1.000

0.154 2 0.020 0.221 0.160 0.108 2 0.026 2 0.018 0.157 0.121 1.000

0.087 0.166 0.008 20.043 20.001 20.066 0.016 20.091 0.043 0.078 1.000

0.157 0.113 0.192 0.170 0.062 0.147 0.082 0.088 0.262 0.094 0.021 1.000

0.158 0.184 0.141 2 0.015 0.133 0.122 0.046 0.021 0.111 0.016 0.172 0.195 1.000

0.141 0.060 20.020 0.002 0.076 0.050 20.013 0.057 0.128 0.103 0.066 20.034 0.204 1.000

0.128 0.291 20.113 20.041 0.228 20.006 20.125 20.048 20.045 20.108 0.103 0.078 0.143 0.039 1.000

Univariate and Multivariate Estimates

Political risk returns Bahamas Canada Costa Rica Cuba Dominican Republic El Salvador Guatemala Haiti Honduras Jamaica Mexico Nicaragua Panama Trinidad and Tobago USA

1.000

447

448

Table 6.16. GARCH(1,1) static conditional correlations for South America by risk return Countries

Argentina

Financial risk returns Argentina 1.000 Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela

Brazil

Chile

Colombia

Ecuador

Paraguay

Peru

Uruguay

Venezuela

0.058 1.000

0.031 20.045 1.000

0.009 0.173 2 0.024 1.000

2 0.084 0.035 0.135 0.198 1.000

2 0.058 0.136 0.038 2 0.026 0.001 1.000

20.081 20.042 0.086 0.120 0.039 20.013 1.000

0.049 0.171 2 0.103 2 0.008 0.122 0.026 0.030 1.000

20.100 0.038 0.089 0.251 0.187 0.010 0.257 20.073 1.000

0.120 20.037 0.043 0.014 0.000 0.071 20.001 0.143 20.018 1.000

0.247 1.000

0.005 20.123 1.000

2 0.056 0.293 2 0.222 1.000

0.015 0.048 0.164 2 0.001 1.000

0.289 0.410 2 0.091 0.137 0.152 1.000

20.044 20.152 0.117 20.064 0.084 20.067 1.000

0.118 0.300 2 0.041 0.189 2 0.058 0.165 2 0.010 1.000

20.056 0.243 20.202 0.442 20.183 0.014 20.084 0.236 1.000

0.042 0.060 0.096 20.044 0.269 0.029 0.042 0.040 20.066 1.000

S. Hoti and M. McAleer

Economic risk returns Argentina 1.000 Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela

Bolivia

Composite risk returns Argentina 1.000 Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela

0.007 1.000

0.105 20.042 1.000

0.116 0.084 2 0.012 1.000

0.173 2 0.060 0.150 0.096 1.000

2 0.047 0.129 0.094 0.135 0.049 1.000

0.172 0.135 20.021 0.173 0.100 0.060 1.000

0.120 0.083 0.054 0.097 0.079 0.153 0.061 1.000

0.275 0.180 0.200 0.192 0.251 0.066 0.224 0.162 1.000

0.357 0.116 0.030 0.160 0.215 0.048 0.204 0.113 0.294 1.000

0.179 1.000

0.063 20.124 1.000

0.042 0.293 2 0.134 1.000

0.000 2 0.024 0.141 0.106 1.000

0.059 0.209 2 0.058 0.166 0.118 1.000

0.048 0.010 0.000 0.115 0.010 20.012 1.000

0.083 0.181 2 0.121 0.050 2 0.027 0.076 0.094 1.000

0.002 0.180 0.147 0.314 0.117 0.062 0.201 20.013 1.000

0.168 0.074 0.057 0.011 0.193 20.012 0.129 0.150 0.068 1.000

Univariate and Multivariate Estimates

Political risk returns Argentina 1.000 Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela

449

450

Table 6.17. GARCH(1,1) static conditional correlations for Sub-Saharan Africa by risk return Economic Risk Returns Countries

Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR Congo Ethiopia Gabon Ghana

Botswana

Burkina Faso

Cameroon

Congo

Coˆte d’Ivoire

DR Congo

Ethiopia

Gabon

Ghana

Guinea

Kenya

Liberia

Malawi

Mali

1.000

0.045 1.000

2 0.114 0.176 1.000

0.073 0.045 0.018 1.000

0.003 0.116 0.187 2 0.065 1.000

20.003 0.137 0.100 0.112 20.001 1.000

0.137 0.332 0.351 0.065 0.191 0.042 1.000

0.057 0.172 0.024 0.112 20.297 0.140 0.157 1.000

0.123 0.068 0.091 0.180 20.065 0.015 0.099 0.013 1.000

20.012 0.096 0.025 0.167 0.004 0.129 0.040 0.092 0.023 1.000

2 0.117 0.028 0.113 2 0.075 0.046 0.064 0.038 0.001 0.157 2 0.176 1.000

0.032 0.129 2 0.021 0.021 0.009 0.239 0.058 0.066 2 0.057 2 0.100 0.173 1.000

2 0.091 0.012 0.072 2 0.126 2 0.004 0.062 0.172 2 0.137 2 0.048 0.044 0.151 0.257 1.000

2 0.027 2 0.083 0.138 0.003 2 0.087 0.077 0.013 0.031 0.187 0.065 2 0.029 2 0.087 0.040 1.000

2 0.055 0.130 0.148 0.066 0.190 2 0.272 0.274 0.047 2 0.049 0.201 2 0.089 2 0.124 0.027 2 0.094 1.000

Mozambique

Nigeria

Senegal

Sierra Leone

South Africa

Sudan

Tanzania

Togo

Uganda

Zambia

Zimbabwe

2 0.091 0.063 0.173 0.000 0.147 0.009 0.300 0.131 0.018 2 0.131

2 0.102 2 0.103 2 0.015 0.041 2 0.257 2 0.126 2 0.178 0.210 0.073 0.174

0.082 0.168 0.082 0.222 0.104 0.260 0.207 0.109 0.111 20.044

20.036 20.078 20.008 20.001 20.004 20.206 0.047 0.008 0.063 0.092

0.077 0.241 0.115 2 0.106 0.141 2 0.008 0.490 0.088 2 0.029 2 0.009

2 0.001 0.033 0.136 2 0.063 0.054 0.124 0.048 0.128 0.070 0.004

0.005 20.023 0.164 0.095 20.042 0.077 0.060 0.059 0.289 0.088

20.020 0.035 0.261 0.199 0.078 20.016 0.160 0.021 0.027 0.170

0.029 0.069 20.136 0.107 0.006 0.005 0.005 0.079 20.154 20.045

0.100 2 0.084 2 0.153 2 0.033 2 0.022 0.024 0.099 0.175 0.193 0.171

2 0.116 2 0.069 0.019 2 0.117 2 0.106 0.024 2 0.293 2 0.088 0.046 0.118

S. Hoti and M. McAleer

Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR Congo Ethiopia Gabon Ghana Guinea Kenya Liberia Malawi Mali

Angola

0.086 0.022 2 0.092 0.037 0.195 1.000

2 0.097 2 0.104 0.002 0.159 2 0.045 2 0.048 1.000

0.139 0.201 20.153 20.023 0.035 0.060 20.266 1.000

20.045 0.040 0.172 0.038 0.048 20.009 20.001 20.132 1.000

0.099 0.193 0.171 0.024 0.163 0.351 2 0.130 0.105 0.018 1.000

0.060 0.050 2 0.062 0.002 2 0.076 0.026 0.063 0.174 2 0.256 2 0.050 1.000

0.091 20.043 0.079 0.363 20.035 0.209 0.138 20.064 0.154 0.059 0.098 1.000

20.012 20.032 0.032 0.133 0.172 0.114 0.119 0.133 0.078 20.006 20.050 0.059 1.000

20.026 0.176 0.071 20.277 0.122 20.020 20.087 0.035 0.027 0.065 20.301 20.210 20.008 1.000

0.041 2 0.035 2 0.058 0.025 2 0.016 0.146 0.135 0.099 2 0.059 0.012 0.033 0.092 2 0.040 2 0.094 1.000

0.066 2 0.042 0.157 0.024 2 0.168 2 0.237 0.079 2 0.106 2 0.037 0.047 2 0.035 0.032 0.003 0.006 2 0.057 1.000

Financial Risk Returns Countries

Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR Congo Ethiopia Gabon Ghana Guinea Kenya Liberia Malawi Mali

Angola

Botswana

Burkina Faso

Cameroon

Congo

Coˆte d’Ivoire

DR Congo

Ethiopia

Gabon

Ghana

Guinea

Kenya

Liberia

Malawi

Mali

1.000

2 0.158 1.000

0.011 20.007 1.000

20.181 0.006 0.061 1.000

0.091 2 0.090 0.220 0.172 1.000

2 0.081 2 0.092 0.133 0.442 0.341 1.000

0.026 0.006 0.192 0.004 20.158 20.220 1.000

2 0.018 2 0.044 0.041 2 0.090 0.018 2 0.058 0.107 1.000

2 0.071 2 0.029 0.262 0.523 0.416 0.567 2 0.159 2 0.045 1.000

0.056 2 0.049 2 0.009 0.098 0.194 0.201 2 0.338 2 0.048 0.320 1.000

0.034 0.035 2 0.131 2 0.045 2 0.011 0.014 0.049 0.132 2 0.045 2 0.025 1.000

0.262 2 0.083 0.146 0.013 0.155 0.148 2 0.182 2 0.006 0.183 0.240 2 0.077 1.000

2 0.162 0.078 2 0.072 2 0.070 2 0.483 2 0.376 0.465 0.205 2 0.464 2 0.435 2 0.005 2 0.319 1.000

0.006 0.036 2 0.019 0.011 0.185 0.224 2 0.166 0.109 0.163 0.180 0.109 0.127 2 0.284 1.000

2 0.195 0.124 2 0.020 0.086 2 0.287 2 0.203 0.453 0.163 2 0.273 2 0.415 0.090 2 0.406 0.765 2 0.234 1.000

451

(continued)

Univariate and Multivariate Estimates

Guinea Kenya Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe

Continued

452

Table 6.17. Nigeria

Senegal

Sierra Leone

South Africa

Sudan

Tanzania

Togo

Uganda

Zambia

Zimbabwe

2 0.013 0.078 0.228 0.092 0.073 0.100 0.069 0.152 0.231 2 0.022 2 0.100 0.058 2 0.064 2 0.089 2 0.057 1.000

2 0.035 0.132 2 0.243 2 0.088 2 0.280 2 0.138 0.147 0.066 2 0.292 2 0.107 0.169 2 0.204 0.299 2 0.050 0.304 2 0.123 1.000

20.166 0.019 0.014 0.186 20.162 20.014 0.343 0.098 20.040 20.351 0.060 20.317 0.566 20.226 0.704 0.063 0.128 1.000

20.341 0.181 20.139 20.116 20.444 20.319 0.379 0.151 20.437 20.347 0.164 20.393 0.662 20.168 0.594 20.143 0.321 0.407 1.000

2 0.057 0.243 0.182 2 0.013 0.124 0.040 0.112 0.133 0.014 2 0.020 0.086 2 0.127 2 0.021 0.061 0.025 0.189 0.012 0.012 0.101 1.000

2 0.095 0.006 0.009 2 0.099 2 0.185 2 0.226 0.289 0.120 2 0.384 2 0.454 2 0.045 2 0.225 0.618 2 0.142 0.596 2 0.071 0.173 0.472 0.468 0.006 1.000

0.209 20.064 20.122 20.029 0.324 0.133 20.394 20.249 0.188 0.481 0.077 0.273 20.652 0.203 20.568 20.128 20.166 20.504 20.487 20.066 20.480 1.000

20.081 0.046 0.106 0.277 0.234 0.114 0.224 0.087 0.131 20.149 0.050 20.233 0.315 20.028 0.502 0.024 0.110 0.560 0.265 0.141 0.490 20.284 1.000

20.124 0.167 20.051 0.046 20.196 20.125 0.290 0.070 20.052 20.176 20.197 20.143 0.358 20.023 0.423 0.042 0.151 0.342 0.227 0.004 0.306 20.277 0.231 1.000

0.049 2 0.042 0.049 2 0.051 2 0.054 0.026 0.006 0.145 2 0.038 2 0.037 0.080 2 0.103 0.033 0.182 0.079 0.053 0.025 0.233 0.035 0.122 0.021 2 0.080 0.145 0.142 1.000

0.023 2 0.006 0.062 2 0.089 2 0.059 2 0.097 0.275 0.097 2 0.229 2 0.267 2 0.027 2 0.159 0.313 0.175 0.285 2 0.195 0.096 0.243 0.275 0.081 0.326 2 0.224 0.201 0.180 0.102 1.000

Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR Congo Ethiopia Gabon Ghana Guinea Kenya Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe

Political Risk Returns Countries

Angola

Botswana

Burkina Faso

Cameroon

Angola Botswana

1.000

0.335 1.000

20.108 0.139

2 0.105 0.130

Congo

0.202 0.087

Coˆte d’Ivoire 0.092 0.240

DR Congo

Ethiopia

Gabon

Ghana

Guinea

Kenya

Liberia

Malawi

Mali

0.231 0.134

0.012 0.054

0.015 0.072

0.193 0.361

20.001 0.026

0.247 0.318

20.051 0.062

2 0.017 0.256

0.023 0.096

S. Hoti and M. McAleer

Mozambique

Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR Congo Ethiopia Gabon Ghana Guinea Kenya Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone South Africa

1.000

0.135 1.000

0.009 2 0.016 1.000

0.126 0.170 0.055 1.000

0.008 20.168 0.006 20.040 1.000

0.229 0.183 0.025 0.059 2 0.095 1.000

0.082 0.055 2 0.061 0.093 2 0.053 0.297 1.000

0.061 20.101 20.016 0.075 0.083 0.019 0.067 1.000

0.054 0.139 20.047 0.011 20.109 0.121 0.224 0.163 1.000

0.182 0.050 20.080 0.056 0.165 20.006 0.040 0.385 0.099 1.000

0.414 0.120 0.040 0.050 20.019 0.023 0.027 20.032 0.015 0.040 1.000

0.103 0.340 0.000 0.220 2 0.069 0.081 0.049 0.191 0.085 0.112 0.098 1.000

0.052 0.049 0.019 2 0.071 0.070 0.153 0.057 2 0.016 0.230 0.044 0.025 0.058 1.000

Mozambique

Nigeria

Senegal

Sierra Leone

South Africa

Sudan

Tanzania

Togo

Uganda

Zambia

Zimbabwe

0.007 0.004 0.090 20.052 0.009 0.007 0.009 0.154 0.105 0.104 0.136 0.042 0.035 0.071 0.254 1.000

0.075 0.091 0.061 0.031 2 0.023 0.058 0.008 2 0.012 0.097 0.145 0.166 0.275 0.013 0.076 0.016 2 0.066 1.000

0.211 0.149 0.142 0.059 0.007 0.128 0.202 0.030 0.028 0.113 2 0.010 0.100 0.022 0.115 0.066 0.187 0.024 1.000

0.109 2 0.001 0.045 0.255 0.107 0.032 2 0.075 2 0.001 0.033 0.016 2 0.151 0.035 0.044 0.083 0.002 0.103 2 0.057 0.031 1.000

0.367 0.410 0.143 0.019 0.157 0.214 0.175 0.094 0.080 0.301 0.094 0.352 0.056 0.147 0.082 0.023 0.073 0.156 0.006 1.000

0.327 0.352 0.077 20.061 0.020 0.282 0.257 20.061 0.016 0.331 20.054 0.403 20.057 0.180 20.059 20.182 0.150 0.093 0.053 0.403

0.113 0.323 0.242 0.157 0.039 0.265 20.138 0.166 0.200 0.347 0.181 0.087 0.055 0.272 20.021 0.108 0.060 0.287 20.032 0.266

0.162 0.150 0.108 0.155 0.050 0.131 0.177 20.045 0.095 0.137 0.035 0.111 0.034 0.072 0.021 0.085 0.055 0.276 0.132 0.172

0.063 0.186 0.195 0.135 0.098 0.111 20.011 0.021 0.015 0.199 0.016 0.136 0.034 0.144 0.021 20.019 0.053 0.046 0.056 0.172

0.353 0.544 0.152 0.071 0.061 0.108 0.053 0.134 0.025 0.502 0.019 0.429 0.004 0.330 0.040 20.048 0.222 0.125 0.019 0.418

0.317 0.359 0.059 0.038 2 0.050 0.087 0.037 0.109 0.153 0.362 0.180 0.303 0.051 0.217 0.043 0.023 0.118 0.038 0.005 0.414

453

(continued)

Univariate and Multivariate Estimates

Burkina Faso Cameroon Congo Coˆte d’Ivoire DR Congo Ethiopia Gabon Ghana Guinea Kenya Liberia Malawi Mali

454

Table 6.17. Continued Mozambique

Nigeria

Senegal

Sierra Leone

South Africa

1.000

Tanzania

Togo

0.206 1.000

0.161 0.190 1.000

Uganda

Zambia

0.144 0.311 0.098 1.000

0.430 0.364 0.175 0.182 1.000

Zimbabwe

0.263 0.322 0.139 0.173 0.555 1.000

Composite Risk Returns Countries

Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR Congo Ethiopia Gabon Ghana Guinea Kenya Liberia Malawi Mali

Angola

Botswana

Burkina Faso

Cameroon

Congo

Coˆte d’Ivoire

DR Congo

Ethiopia

Gabon

Ghana

Guinea

Kenya

Liberia

Malawi

Mali

1.000

0.219 1.000

20.135 0.074 1.000

20.011 0.156 0.070 1.000

0.118 0.094 0.275 0.103 1.000

0.038 0.210 0.264 0.237 0.096 1.000

0.133 0.116 0.184 20.001 0.045 0.037 1.000

0.055 0.048 20.026 0.001 20.208 0.015 20.029 1.000

0.106 0.094 0.127 0.262 0.084 0.192 0.158 0.025 1.000

0.118 0.226 0.070 0.118 2 0.014 0.118 0.088 0.051 0.147 1.000

0.044 0.045 0.033 2 0.030 2 0.026 0.098 2 0.047 0.062 0.148 2 0.171 1.000

0.214 0.171 0.116 0.022 0.100 0.186 0.090 0.053 0.025 0.079 0.057 1.000

20.210 20.022 0.301 0.003 20.049 0.128 0.087 0.036 20.114 20.051 0.029 20.047 1.000

0.079 0.126 0.084 0.185 20.042 0.111 0.105 0.126 0.176 0.151 20.011 0.039 0.024 1.000

2 0.164 0.032 0.206 0.160 0.090 2 0.084 0.138 0.088 0.149 0.150 2 0.045 2 0.050 0.030 0.045 1.000

S. Hoti and M. McAleer

Sudan Tanzania Togo Uganda Zambia Zimbabwe

Sudan

Nigeria

Senegal

Sierra Leone

South Africa

Sudan

Tanzania

Togo

Uganda

Zambia

Zimbabwe

0.077 0.105 0.101 0.066 0.188 0.085 0.038 0.129 0.091 2 0.030 0.095 0.131 2 0.042 0.043 0.089 1.000

2 0.058 2 0.051 2 0.033 0.043 2 0.210 0.004 2 0.039 0.101 0.009 0.028 0.020 2 0.108 0.014 0.150 0.055 2 0.114 1.000

0.158 0.076 0.041 0.098 0.020 0.174 0.081 20.014 0.183 0.035 0.055 0.100 20.024 0.079 0.001 0.148 20.138 1.000

20.180 20.073 20.073 0.071 20.046 20.093 20.016 0.088 0.036 0.042 20.055 20.094 0.125 0.187 0.050 0.037 0.022 20.162 1.000

0.175 0.331 0.189 2 0.158 0.177 0.140 0.184 0.063 0.025 0.125 0.148 0.351 2 0.055 0.043 2 0.003 0.127 2 0.097 0.119 0.006 1.000

0.075 0.177 0.188 2 0.067 2 0.001 0.170 0.171 0.084 0.028 0.049 0.007 0.108 2 0.064 0.105 0.029 2 0.100 0.137 0.133 2 0.080 0.228 1.000

0.255 0.050 20.004 0.037 0.035 0.056 0.030 0.003 0.158 0.205 0.009 0.042 20.033 0.215 20.105 0.080 20.067 0.130 0.059 0.111 0.044 1.000

0.101 0.049 0.082 0.162 0.093 0.034 0.081 0.040 0.112 0.167 20.100 0.001 20.060 0.214 0.098 0.138 20.008 0.158 0.115 0.098 0.090 0.007 1.000

20.022 0.073 0.019 0.170 20.002 0.056 20.025 0.092 20.087 0.097 20.021 0.146 0.062 20.027 0.063 0.014 20.044 0.061 0.012 0.094 20.050 20.029 20.037 1.000

0.184 0.250 0.040 0.004 0.000 0.044 0.191 0.108 0.136 0.238 0.112 0.186 2 0.086 0.141 0.106 0.093 0.131 0.233 2 0.064 0.352 0.241 0.096 0.134 0.005 1.000

0.074 0.162 0.006 2 0.058 2 0.072 0.073 0.035 0.253 0.102 0.140 0.160 0.064 2 0.043 0.209 0.036 0.003 2 0.064 0.173 2 0.090 0.320 0.126 0.127 0.074 0.084 0.299 1.000

Univariate and Multivariate Estimates

Angola Botswana Burkina Faso Cameroon Congo Coˆte d’Ivoire DR Congo Ethiopia Gabon Ghana Guinea Kenya Liberia Malawi Mali Mozambique Nigeria Senegal Sierra Leone South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe

Mozambique

455

456

Table 6.18. GARCH(1,1) static conditional correlations for West Europe by risk return Economic Risk Returns Countries

Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal Spain Sweden

Belgium

Cyprus

Denmark

Finland

France

Germany

Greece

Iceland

Ireland

Italy

Luxembourg

Malta

Netherlands

Norway

Portugal

1.000

0.135 1.000

0.132 0.331 1.000

0.038 0.114 0.115 1.000

20.028 0.094 20.013 0.173 1.000

0.067 0.128 0.292 0.086 0.061 1.000

0.139 0.039 0.079 0.035 0.099 0.188 1.000

20.087 0.037 0.058 0.147 0.151 0.139 20.005 1.000

20.013 20.001 0.036 20.073 20.116 0.098 0.104 0.052 1.000

2 0.100 0.239 0.062 0.078 0.126 0.207 0.186 0.080 0.210 1.000

0.171 0.156 0.276 0.015 0.066 0.332 0.329 0.159 0.184 0.142 1.000

0.002 0.282 0.091 0.187 0.164 0.288 0.350 0.057 0.152 0.214 0.184 1.000

0.264 0.082 0.163 0.057 20.120 0.234 0.167 20.041 0.024 0.081 0.234 0.140 1.000

0.004 0.146 0.172 20.006 0.097 0.161 0.045 0.134 0.084 0.223 0.054 0.007 0.053 1.000

0.202 0.134 0.006 0.104 0.173 0.062 0.052 2 0.072 2 0.129 2 0.100 2 0.086 0.221 2 0.068 2 0.124 1.000

0.348 0.221 0.146 0.081 20.038 0.074 0.028 20.010 20.005 20.048 0.176 20.055 0.236 0.211 0.141 1.000

Spain

Sweden

Switzerland

Turkey

UK

0.125 0.052 0.160 0.170 0.072 0.319 0.211 0.002 0.040 0.197 0.302 0.137 0.343 0.132 20.017 0.214 1.000

0.024 2 0.030 0.122 2 0.013 0.065 0.089 0.010 0.032 0.164 0.082 0.055 2 0.015 2 0.170 0.302 0.121 0.119 0.089 1.000

20.060 0.157 0.006 0.041 0.127 0.166 0.330 20.029 0.093 0.275 0.321 0.292 0.127 0.111 20.171 20.144 0.176 20.017

0.110 2 0.005 0.130 0.054 0.029 0.048 2 0.136 2 0.001 2 0.091 2 0.070 2 0.076 2 0.062 2 0.152 0.002 0.214 0.029 2 0.037 0.242

20.108 0.041 20.033 20.113 0.086 0.091 0.070 0.136 0.034 0.136 20.050 0.057 20.137 0.259 0.014 20.005 20.087 0.217

S. Hoti and M. McAleer

Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal

Austria

Switzerland Turkey UK

1.000

2 0.297 1.000

20.005 0.050 1.000

Financial Risk Returns Countries

Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands

Belgium

Cyprus

Denmark

Finland

France

Germany

Greece

Iceland

Ireland

Italy

Luxembourg

Malta

Netherlands

Norway

Portugal

1.000

0.742 1.000

0.544 0.642 1.000

0.622 0.756 0.733 1.000

0.758 0.786 0.575 0.690 1.000

0.753 0.843 0.670 0.736 0.809 1.000

0.684 0.734 0.528 0.640 0.730 0.783 1.000

0.652 0.561 0.420 0.483 0.600 0.587 0.496 1.000

0.519 0.563 0.368 0.480 0.504 0.559 0.458 0.516 1.000

0.694 0.732 0.643 0.730 0.722 0.731 0.598 0.617 0.467 1.000

0.659 0.601 0.407 0.506 0.671 0.728 0.621 0.521 0.569 0.635 1.000

0.688 0.781 0.577 0.745 0.795 0.779 0.715 0.457 0.524 0.665 0.602 1.000

0.444 0.567 0.698 0.680 0.509 0.589 0.445 0.409 0.373 0.564 0.368 0.526 1.000

0.636 0.807 0.582 0.793 0.708 0.743 0.683 0.434 0.541 0.681 0.553 0.816 0.436 1.000

0.226 0.114 0.265 0.204 0.292 0.184 0.210 0.087 0.050 0.266 0.230 0.185 0.312 20.052 1.000

0.743 0.743 0.535 0.679 0.719 0.741 0.696 0.649 0.550 0.717 0.677 0.711 0.495 0.730 0.184 1.000

Spain

Sweden

Switzerland

Turkey

UK

0.645 0.626 0.496 0.558 0.694 0.712 0.629 0.564 0.457 0.694 0.769 0.655 0.446 0.578

0.554 0.656 0.495 0.591 0.711 0.609 0.560 0.500 0.567 0.526 0.522 0.582 0.408 0.628

0.387 0.455 0.279 0.404 0.390 0.408 0.380 0.189 0.324 0.358 0.376 0.453 0.260 0.481

0.357 0.415 0.360 0.529 0.338 0.372 0.239 0.369 0.440 0.414 0.255 0.280 0.500 0.436

0.490 0.647 0.452 0.550 0.623 0.630 0.531 0.461 0.499 0.485 0.524 0.463 0.423 0.557

457

(continued)

Univariate and Multivariate Estimates

Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal

Austria

458

Table 6.18. Norway Portugal Spain Sweden Switzerland Turkey UK

Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal

Austria Belgium Cyprus Denmark Finland

Spain

Sweden

Switzerland

Turkey

UK

0.379 0.704 1.000

0.103 0.544 0.490 1.000

0.015 0.418 0.359 0.273 1.000

2 0.079 0.349 0.193 0.408 0.273 1.000

0.054 0.531 0.479 0.624 0.418 0.396 1.000

Political Risk Returns Austria

Belgium

Cyprus

Denmark

Finland

France

Germany

Greece

Iceland

Ireland

Italy

Luxembourg

Malta

Netherlands

Norway

Portugal

1.000

0.019 1.000

0.267 0.180 1.000

0.080 0.060 0.133 1.000

0.098 2 0.020 0.142 0.116 1.000

0.247 20.054 0.304 0.120 0.198 1.000

20.028 0.006 0.188 20.041 20.074 0.123 1.000

20.002 0.053 0.075 0.088 0.028 0.221 0.094 1.000

0.107 0.054 0.028 2 0.017 0.168 0.072 0.014 0.105 1.000

20.005 0.023 0.079 0.160 0.219 0.183 20.053 0.127 0.089 1.000

0.129 0.129 0.174 0.062 0.137 0.132 0.169 0.140 2 0.008 0.101 1.000

0.195 0.148 0.093 0.148 20.091 20.073 0.037 20.065 0.067 20.037 0.186 1.000

0.144 0.074 0.337 0.106 0.054 0.328 0.108 0.046 0.094 0.032 0.077 20.005 1.000

0.031 0.001 20.070 0.164 0.053 0.148 0.066 0.168 0.012 0.033 0.144 0.062 0.052 1.000

20.008 0.031 0.006 0.082 0.053 20.036 0.086 0.112 0.002 20.030 0.179 0.017 0.037 0.129 1.000

20.147 20.095 0.007 0.095 0.035 20.085 0.028 0.076 0.120 0.069 0.083 0.011 0.099 0.018 20.001 1.000

Spain

Sweden

20.002 20.064 0.207 0.112 0.012

0.258 0.110 0.119 0.173 0.103

Switzerland 0.147 0.031 0.097 0.196 0.090

Turkey

UK

0.212 20.096 0.214 0.140 0.120

20.004 0.034 0.085 0.031 0.128

S. Hoti and M. McAleer

Countries

Continued

0.195 0.117 0.012 0.065 0.084 0.056 0.104 0.214 0.097 20.009 0.236 1.000

0.220 0.028 0.049 0.101 0.044 0.319 0.251 0.162 0.266 0.179 0.011 0.184 1.000

0.159 20.013 0.182 0.078 0.070 0.278 0.199 0.028 0.099 0.068 0.077 0.071 0.178 1.000

0.442 20.031 0.182 0.059 0.046 0.130 20.058 0.533 0.022 0.004 20.002 0.124 0.155 0.209 1.000

0.101 0.051 20.064 0.136 0.100 20.009 0.067 0.178 0.040 20.101 0.185 0.232 0.162 0.086 0.192 1.000

Composite Risk Returns Countries Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal

Austria

Belgium

Cyprus

Denmark

Finland

France

Germany

Greece

Iceland

Ireland

Italy

Luxembourg

Malta

Netherlands

Norway

Portugal

1.000

0.367 1.000

0.464 0.439 1.000

0.231 0.447 0.391 1.000

0.253 0.262 0.264 0.364 1.000

0.360 0.222 0.410 0.336 0.338 1.000

0.154 0.246 0.194 0.122 0.195 0.224 1.000

0.189 0.240 0.184 0.197 0.226 0.318 0.016 1.000

0.160 0.074 0.079 0.029 0.030 0.122 2 0.031 0.168 1.000

0.094 0.202 0.109 0.200 0.223 0.221 0.016 0.177 0.122 1.000

0.287 0.293 0.211 0.182 0.254 0.309 0.237 0.173 0.093 0.258 1.000

0.171 0.411 0.356 0.357 0.189 0.204 0.268 0.036 0.085 0.164 0.216 1.000

0.233 0.384 0.452 0.473 0.274 0.314 0.123 0.161 0.072 0.137 0.118 0.276 1.000

0.219 0.448 0.227 0.491 0.255 0.327 0.152 0.253 20.001 0.122 0.134 0.264 0.340 1.000

0.056 0.108 0.082 0.197 0.165 0.040 0.097 0.105 20.054 20.016 0.167 0.034 0.099 0.246 1.000

0.155 0.224 0.087 0.279 0.219 0.130 0.158 0.238 0.213 0.172 0.174 0.102 0.257 0.258 0.163 1.000

Univariate and Multivariate Estimates

France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal Spain Sweden Switzerland Turkey UK

(continued)

459

460

Table 6.18. Spain

Sweden

Switzerland

Turkey

UK

0.156 0.198 0.253 0.380 0.247 0.375 0.234 0.104 0.107 0.230 0.324 0.270 0.390 0.263 0.051 0.293 1.000

0.349 0.284 0.257 0.328 0.250 0.357 0.097 0.210 0.137 0.120 0.281 0.174 0.182 0.430 0.237 0.156 0.209 1.000

0.228 0.273 0.193 0.186 0.111 0.205 0.095 0.097 0.088 0.188 0.326 0.279 0.122 0.164 20.048 0.113 0.293 0.124 1.000

0.201 0.021 0.230 0.232 0.141 0.297 20.084 0.258 0.098 20.005 0.037 20.047 0.225 0.105 20.048 20.053 0.140 0.198 0.016 1.000

0.111 0.180 0.143 0.079 0.184 0.215 0.203 0.026 0.100 0.078 0.101 0.093 0.146 0.114 0.036 0.188 0.195 0.215 0.067 0.139 1.000

S. Hoti and M. McAleer

Austria Belgium Cyprus Denmark Finland France Germany Greece Iceland Ireland Italy Luxembourg Malta Netherlands Norway Portugal Spain Sweden Switzerland Turkey UK

Continued

Univariate and Multivariate Estimates

461

Table 6.19. Range of conditional correlation coefﬁcients for region by risk return Regions

Risk Returns

Range

Central and South Asia

Economic Financial Political Composite

2 0.084, 0.258 2 0.050, 0.259 0.025, 0.245 0.093, 0.328

East Asia and the Paciﬁc

Economic Financial Political Composite

2 0.270, 0.431 2 0.475, 0.608 2 0.177, 0.341 2 0.296, 0.451

East Europe

Economic Financial Political Composite

2 0.218, 0.475 2 0.391, 0.737 2 0.106, 0.454 2 0.077, 0.511

Middle East and North Africa

Economic Financial Political Composite

2 0.302, 0.568 2 0.189, 0.685 2 0.251, 0.704 2 0.241, 0.802

North and Central America

Economic Financial Political Composite

2 0.216, 0.327 2 0.635, 0.602 2 0.125, 0.329 2 0.170, 0.351

South America

Economic Financial Political Composite

2 0.103, 0.257 2 0.222, 0.442 2 0.060, 0.357 2 0.134, 0.314

Sub-Saharan Africa

Economic Financial Political Composite

2 0.301, 0.490 2 0.652, 0.765 2 0.182, 0.555 2 0.210, 0.352

West Europe

Economic Financial Political Composite

2 0.297, 0.350 2 0.079, 0.843 2 0.147, 0.533 2 0.084, 0.491

462

S. Hoti and M. McAleer

Table 6.20. Number of conditional correlations for central and South Asia by range Risk Returns

Range

Number

Economic

2 0.100, 2 0.001 0.000, 0.099 0.100, 0.199 0.200, 0.299

2 1 1 2

Financial

2 0.100, 2 0.001 0.000, 0.099 0.100, 0.199 0.200, 0.299

1 2 2 1

Political

0.000, 0.099 0.100, 0.199 0.200, 0.299

1 2 3

Composite

0.000, 0.100, 0.200, 0.300,

1 2 2 1

0.099 0.199 0.299 0.399

Table 6.21. Number of conditional correlations for East Asia and the Paciﬁc by range Risk Returns

Range

Number

Economic

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499

2 8 20 35 41 18 11 1

Financial

20.500, 20.400, 20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400,

2 0.401 2 0.301 2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499

2 9 10 9 11 24 7 15 20 16 (continued)

Univariate and Multivariate Estimates

Table 6.21. Risk Returns

463

Continued Range

Number

0.500, 0.599 0.600, 0.699

11 2

Political

20.200, 20.100, 0.000, 0.100, 0.200, 0.300,

2 0.101 2 0.001 0.099 0.199 0.299 0.399

10 33 45 38 9 1

Composite

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499

1 1 23 40 46 19 5 1

Table 6.22. Number of conditional correlations for East Europe by range Risk Returns

Range

Number

Economic

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499

1 2 4 9 9 6 3 2

Financial

20.400, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500, 0.600, 0.700,

2 0.301 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499 0.599 0.699 0.799

2 4 4 3 8 2 3 5 2 2 1 (continued)

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S. Hoti and M. McAleer

Table 6.22. Risk Returns

Continued Range

Number

Political

20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400,

2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499

1 5 9 6 10 4 1

Composite

20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500,

2 0.001 0.099 0.199 0.299 0.399 0.499 0.599

7 8 9 8 2 1 1

Table 6.23. Number of conditional correlations for Middle East and North Africa by range Risk Returns

Range

Number

Economic

20.400, 20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500,

2 0.301 2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499 0.599

1 2 8 18 28 29 39 17 8 3

Financial

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500, 0.600,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499 0.599 0.699

1 8 12 20 25 34 25 18 7 3

Political

20.300, 2 0.201 20.200, 2 0.101

1 4 (continued)

Univariate and Multivariate Estimates

Table 6.23. Risk Returns

Composite

465

Continued Range

Number

20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500, 0.600, 0.700,

2 0.001 0.099 0.199 0.299 0.399 0.499 0.599 0.699 0.799

8 5 16 33 44 24 10 7 1

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500, 0.600, 0.800,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499 0.599 0.699 0.899

1 2 5 7 11 22 60 32 9 3 1

Table 6.24. Number of conditional correlations for North and Central America by range Risk Returns

Range

Number

Economic

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399

1 19 21 32 25 5 2

Financial

20.700, 20.500, 20.400, 20.300, 20.200, 20.100, 0.000, 0.100, 0.200,

2 0.601 2 0.401 2 0.301 2 0.201 2 0.101 2 0.001 0.099 0.199 0.299

2 5 4 10 13 15 12 13 13 (continued)

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Table 6.24. Risk Returns

Continued Range

0.300, 0.400, 0.500, 0.600,

0.399 0.499 0.599 0.699

Number 7 7 3 1

Political

20.200, 20.100, 0.000, 0.100, 0.200, 0.300,

2 0.101 2 0.001 0.099 0.199 0.299 0.399

1 10 30 42 17 5

Composite

20.200, 20.100, 0.000, 0.100, 0.200, 0.300,

2 0.101 2 0.001 0.099 0.199 0.299 0.399

4 18 35 36 10 2

Table 6.25. Number of conditional correlations for South America by range Risk Returns

Range

Number

Economic

20.200, 20.100, 0.000, 0.100, 0.200,

2 0.101 2 0.001 0.099 0.199 0.299

2 13 18 10 2

Financial

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.400,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.499

2 3 13 11 7 6 2

Political

20.100, 0.000, 0.100, 0.200, 0.300,

2 0.001 0.099 0.199 0.299 0.399

5 14 18 7 1

Composite

20.200, 2 0.101 20.100, 2 0.001

3 6 (continued)

Univariate and Multivariate Estimates

Table 6.25. Risk Returns

467

Continued Range

0.000, 0.100, 0.200, 0.300,

0.099 0.199 0.299 0.399

Number 18 14 3 1

Table 6.26. Number of conditional correlations for Sub-Saharan Africa by range Risk Returns

Range

Number

Economic

20.400, 20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400,

2 0.301 2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499

1 10 25 75 124 72 12 5 1

Financial

20.700, 20.600, 20.500, 20.400, 20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500, 0.600, 0.700,

2 0.601 2 0.501 2 0.401 2 0.301 2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499 0.599 0.699 0.799

1 2 10 12 20 35 63 67 53 28 13 10 7 2 2

Political

20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500,

2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499 0.599

8 38 138 80 29 21 8 3 (continued)

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Table 6.26. Risk Returns Composite

Continued Range

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399

Number 3 13 61 141 81 21 5

Table 6.27. Number of conditional correlations for West Europe by range Risk Returns

Range

Number

Economic

20.300, 20.200, 20.100, 0.000, 0.100, 0.200, 0.300,

2 0.201 2 0.101 2 0.001 0.099 0.199 0.299 0.399

1 14 36 67 57 25 10

Financial

20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500, 0.600, 0.700, 0.800,

2 0.001 0.099 0.199 0.299 0.399 0.499 0.599 0.699 0.799 0.899

2 4 7 14 20 40 44 42 34 3

Political

20.200, 20.100, 0.000, 0.100, 0.200, 0.300, 0.400, 0.500,

2 0.101 2 0.001 0.099 0.199 0.299 0.399 0.499 0.599

1 34 84 69 16 4 1 1

Composite

20.100, 0.000, 0.100, 0.200, 0.300, 0.400,

2 0.001 0.099 0.199 0.299 0.399 0.499

9 35 69 65 22 10

Univariate and Multivariate Estimates

469

Table 6.28. Ranking by range of variation in the conditional correlations Region

Central and South Asia East Asia and the Paciﬁc East Europe Middle East and North Africa North and Central America South America Sub-Saharan Africa West Europe

Ranking 1

2

3

4

POL POL POL ECO POL ECO COM COM

COM ECO COM FIN COM POL POL ECO

FIN COM ECO POL ECO COM ECO POL

ECO FIN FIN COM FIN FIN FIN FIN

Notes: (1) The lowest conditional correlation range is denoted as 1 and the highest as 4. (2) Economic, ﬁnancial, political and composite risk returns are denoted as ECO, FIN, POL and COM, respectively.

CHAPTER 7

Conclusion Abstract This chapter summarizes the qualitative assessment of country risk literature, agency risk rating systems and country risk ratings and risk returns for 120 selected countries, and the theoretical and empirical results relating to various univariate and multivariate risk returns and corresponding constant conditional correlation coefﬁcients. Suggestions for future research are also presented. Keywords: risk, country proﬁles, associated ratings, rating systems, agency ratings, conditional volatility models, statistical and econometric criteria, estimation, evaluation JEL classiﬁcations: C22, C51, E44, F34, O16, O57 7.1. Summary of the monograph This monograph has examined the concept of country risk and its associated risk ratings, and presented an econometric analysis of the riskiness in country risk ratings. Chapter 2 provided a detailed analysis of the empirical foundations of the published contributions to the literature on country risk. The examination of the empirical impact and statistical signiﬁcance of the results of the country risk models was based on an evaluation of the descriptive statistics relating to the models, as well as the econometric procedures used in estimation, testing and forecasting. Overall, country risk studies have been based on pooled or cross-section type of data. The two most frequently used dependent variables were the probability of debt rescheduling and agency country risk ratings. There were three types of explanatory variables used, namely economic, ﬁnancial and political variables. More than two-thirds of the omitted explanatory variables and proxy variables used were economic and ﬁnancial in nature. In terms of the preferred country risk model, logit followed by probit

472

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and discriminant were the most popular models. However, there was a disparity between the model speciﬁcation examined and the appropriate method of estimation. Moreover, diagnostic testing had generally been ignored in the country risk literature. Overall, in the absence of testing the validity of the underlying assumptions of the surveyed country risk models, the empirical results should generally be interpreted with both caution and scepticism. Chapter 3 provided a qualitative comparison of the risk rating systems of 10 leading commercial agencies of country risk, namely Business Environment Risk Intelligence S.A, Economist Intelligence Unit, Euromoney, Fitch IBCA, Institutional Investor, International Country Risk Guide (ICRG), Moody’s, Political Risk Services, S.J. Rundt and Associates and Standard and Poor’s. Classiﬁcations of the 10 risk rating agencies were given according to the agency deﬁnition of country risk ratings, number of countries covered, frequency of the risk ratings, number and type of ratings compiled, number and type of risk component variables used, weights assigned to risk components and the given range for the risk ratings. Moreover, the rating system of the ICRG was analysed in detail. The ICRG was selected as a representative of the rating agencies and is the only risk rating agency to provide consistent monthly risk ratings. Such an evaluation permitted a critical assessment of the importance and relevance of agency rating systems, and a critical comparison of the 10 agency rating systems. In Chapter 4, monthly ICRG country risk ratings and risk returns were assessed for 120 countries by geographic region. A detailed evaluation of ICRG risk ratings and risk returns, where the latter was deﬁned as the monthly percentage change in the respective risk ratings, was provided. For each of the 120 countries, the trends and associated volatility of the four country risk ratings and risk returns were analysed according to economic, ﬁnancial and political environments in the country. There were substantial changes in the trends of the risk ratings, as well as in their associated volatilities for the 120 countries across the eight geographic regions. Similarly, substantial differences were evident in the risk returns, as well as in their volatilities. Chapter 4 provided, for the ﬁrst time, a comparative assessment of the trends and volatility of country risk ratings for 120 countries, and highlighted the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating. Chapter 5 reviewed the most recent theoretical results on univariate GARCH models of conditional volatility, and discussed the constant correlation asymmetric VARMA–GARCH model. The underlying structure of the VARMA–AGARCH model was examined, including convenient sufﬁcient conditions for the existence of moments for empirical analysis. Alternative sufﬁcient conditions for the consistency and asymptotic

Conclusion

473

normality of the QMLE were given under non-normality of the standardized shocks. These conditions permit an empirical evaluation of the usefulness of the models for analysing country risk ratings and risk returns, and their associated volatility. As risk ratings can be treated as indexes, their rates of change, or risk returns, were analysed in Chapter 6 in the same manner as ﬁnancial returns. The empirical results provided a comparative assessment of the conditional means and volatilities associated with country risk returns across alternative risk returns and countries over time, enabled a validation of the regularity conditions underlying the model, highlighted the importance of economic, ﬁnancial and political risk ratings as components of a composite risk rating, and evaluated the usefulness of the ICRG risk ratings. In particular, at the univariate level for both the symmetric GARCH and asymmetric GJR models, the sufﬁcient parametric conditions for the estimated volatilities to be positive were generally satisﬁed, as were the log– moment and second moment conditions for the QMLE to be consistent and asymptotically normal. Therefore, the univariate GARCH(1,1) and GJR(1,1) models were found to be statistically adequate for the risk returns of the 120 countries. Based on the monthly standardized residuals of the univariate AR(1)– GARCH(1,1) models, the corresponding static conditional correlations were calculated for the economic, ﬁnancial, political and composite risk return shocks for the 120 countries. Overall, the rankings by range of variation of the GARCH(1,1) static conditional correlations across the eight regions are always the highest for ﬁnancial, economic or composite risk returns, and always the lowest for political, economic or composite risk returns. The rankings by range of variation of the conditional correlations for ﬁnancial risk returns are never the lowest and those for political risk return are never the highest. 7.2. Future research There are two ways in which the research undertaken in this monograph can be extended, namely a consideration of alternative methods, models and data, as well as new research directions. 7.2.1. Alternative methods, models and data (1) Rolling and recursive estimation of univariate and multivariate symmetric and asymmetric volatility models. (2) Estimation of univariate and multivariate models with alternative distributional assumptions regarding the standardized residuals.

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(3) Estimating and testing VARMA – GARCH and VARMA– AGARCH models with time-varying conditional correlations. (4) Evaluating the effects of changing the number of risk ratings, risk returns and countries on the conditional correlations arising from each of the models and methods given above. 7.2.2. New research directions (1) Application of univariate and multivariate stochastic volatility models to risk ratings and risk returns. (2) Estimation and testing of conditional volatility and stochastic volatility models with known and unknown structural breakpoints. (3) Adaptation of the methods used in this monograph to the probability of debt rescheduling, which is an important focus variable in the country risk literature. (4) Empirical analysis of the qualitative ratings provided by leading risk rating agencies based on categorical data, and a critical comparison of the time series ICRG data used in this monograph with alternative ordered models using such categorical data. (5) Development of a novel index which incorporates information on environmental performance and sustainability, as well as intellectual property innovation and protection, to complement the existing economic, ﬁnancial and political components in the construction of a composite country risk rating. 7.3. Conclusion This chapter summarized the qualitative assessment of country risk literature, agency risk rating systems and country risk ratings and risk returns for 120 selected countries, and the theoretical and empirical results relating to various univariate and multivariate risk returns and corresponding constant conditional correlation coefﬁcients. Suggestions for future research were also presented.

AUTHOR INDEX Abassi, B. 31, 69, 91 Abdullah, F.A. 28, 32, 70 Amato, J.D. 96, 109 Backer, A. 29, 33, 70 Balkan, E.M. 29, 34, 71 Bates, P.S. 3, 8 –10, 31 Bera, A.K. 339, 345 Berndt, E.K. 350, 377 Bettis, R.A. 30, 63, 87 Bhatia, A.V. 95– 99, 109 Bollerslev, T. 338 –342, 346, 350– 351, 363, 377, 379, 387, 391, 399, 406, 411, 423, 433 de Bondt, G.J. 29, 34, 71 Boussama, F. 341, 344, 346 Bourke, P. 2, 8 Brewer, T.L. 29, 31, 35, 55, 66, 71, 89 Burton, F.N. 29, 36, 71, 95, 109 Cantor, R. 29, 37, 72 Chan, F. 6, 8, 337, 346 – 347, 363, 377 Chattopadhyay, S.P. 29, 38, 72 Chou, R.Y. 339, 346 Citron, J.T. 29, 38, 72 Cline, W.R. 29, 45, 77 Comte, F. 341 –342, 346 Cooper, J.C.B. 29, 39, 73 Cosset, J.C. 29 –30, 39, 64, 73, 87 Daouas, M. 30, 64, 87 Deng, W.C. 341, 347 Ding. Z. 341, 346

Dodd, R. 95, 110 Doumpos, M. 29, 40, 73 Easton, S.T. 29, 40, 74 Eaton, J. 3, 8 –10, 29, 41– 42, 74 –75 Edwards, S. 29, 42, 75 Elie, L. 341, 346 Engle, R.F. 338 –339, 341 –342, 346 Erb, C.B. 29, 43, 76 Feder, J. 29, 43 –44, 76 –77 Frank, C.R. 29, 45, 77 Furfine, C.H. 96, 109 George, D.A.R. 30, 345 Gersovitz, M. 29, 41 – 42, 74 –75 Ghose, T.K. 2, 8 Glosten, L. 338, 346, 350, 377 Granger, C.W.J. 341, 346 de Haan, J. 29, 45, 78 Haber, L.J. 31, 65, 87 Hall, B.H. 350, 377 Hall, R.E. 350, 377 Hajivassiliou, V.A. 29, 46, 78 Haque, N.U. 30, 47, 79 Hansen, B.E. 344, 346 Harvey, C.R. 29, 43, 76 Hausman, J.A. 350, 377 Hayes, N.L. 94, 110 He, C. 340, 346 Hernandez –Trillo, F. 30, 49, 79 Higgins, M.L. 339, 345

476

Author Index

Hoti, S. 2 – 4, 6, 8 –11, 16 –17, 20, 22, 24, 26 –27, 30, 94, 110, 337– 338, 341, 343– 346, 363, 377 Howell, L.D. 97 – 98, 100 – 102, 104, 110 Inoue, H. 29, 36, 71, 95, 109 Jagannathan, R. 338, 346, 350, 377 Jeantheau, T. 341– 342, 344, 346 Just, R.E. 29, 43 –44, 76 –77 Juttner, D.J. 3, 8 Kettani, O. 30, 64, 87 Kharas, H. 30, 49, 80 Kraft, D.F. 341, 346 Krayenbuehl, T.E. 2, 10 Kroner, K.F. 339, 341 –342, 346 Kugler, P. 30, 50, 80 Kumar, M.S. 30, 47, 79 Kutty, G. 30, 50, 80 – 81 Lange, H. 2, 8 Lanoie, P. 30, 51, 81 Lee, B.C. 30, 53, 81 Lee, S.H. 30, 53 –56, 82 –83 Lee, S.W. 344, 346 Lemarbre, S. 30, 51, 81 Li, W.K. 339, 341, 346 – 347 Lieberman, O. 341– 342, 346 Ling, S. 341– 342, 344, 346– 347, 363, 377 Lloyd – Ellis, H. 30, 33, 57 – 58, 70, 83– 84 van Lubek, E. 29, 45, 78 Marinova, D. 339, 341, 344, 347 Mascarenhas, B. 30, 59, 85 Mark, N. 30, 47, 79 Mathieson, D.J. 30, 47, 79 McAleer, M. 2– 4, 6, 8, 10 –11, 15– 17, 20, 22, 24 – 27, 30, 94, 110, 337– 347, 363, 377

McFadden, D.L. 346 McKenzie, G.W. 30, 33, 57 –58, 70, 83 –84 Moon, C.G. 30, 60, 86 Morgan, J.B. 30, 61, 86 Nagy, P.J. 3, 8 Nelson, D.B. 339– 340, 344, 346, 347 Nickelsburg, G. 29, 38, 72 Odedokun, M.O. 30, 62, 87 Oetzel, J.M. 30, 63, 87 Oral, M. 30, 64, 87 Overholt, W.H. 2, 8 Oxley, L. 30, 345– 346 Packer, F. 29, 37, 72 Pantula, S.G. 340, 347 Powell, J.G. 30, 53, 81 Rahnama – Moghadam, M. 31, 65, 87 –88 Ramcharran, H. 31, 66, 89 Rivoli, P. 29, 31, 35, 55, 66, 71, 89 Roberts, C.J. 30, 345 Rockerbie, D.W. 3, 9 –10, 40, 74 Rodrigues, A.P. 341 Ross, K. 44, 77 Roy, J. 39, 73 Runkle, D. 338, 346, 350, 377 Saini, K.G. 3, 8– 10, 31 Samavati, H. 2 Sand, O.C. 30, 59, 85 Saunders, A. 2 Sayer, S. 30, 345 Schmidt, R. 31, 67, 89 Scholtens, B. 31, 68, 90 Setty, G. 95, 110 Shanmugam, B. 2 –3, 8 Siermann, C.L.J. 29, 45, 78

Author Index

Somerville, R.A. 31, 68, 90 Stotsky, J.G. 30, 60, 86 Taffler, R.J. 31, 68 –69, 90 –91 Taylor, L. 3, 8– 10, 29 Tera¨ svirta, T. 340, 346 Thomas, S.H. 30, 33, 57 – 58, 70, 83– 84 Uy, L.W. 29, 44, 77

Viskanta, T.E. 29, 43, 76 Weiss, A.A. 340, 347 Winder, C.C.A. 29, 34, 71 Wong, H. 341, 347 Wooldridge, J.M. 341, 346, 351, 377, 379, 387, 391, 399, 406, 411, 423, 433 Zenner, M. 30, 63, 87 Zopounidis, C. 29, 40, 73

477

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COUNTRY INDEX Albania 112 –113, 132 –133, 234, 333, 355, 367– 368, 388, 435, 442 Algeria 112, 140, 243, 333, 356– 357, 368– 369, 392, 435, 443– 445 Angola 112 – 113, 174 –175, 178– 179, 286, 334, 361, 372, 412, 436, 450– 455 Argentina 112, 166– 167, 171– 173, 276, 334, 359, 371 – 372, 407, 436, 448– 449 Australia 112, 118 –119, 125 –127, 217, 333, 353 – 354, 365– 366, 380, 434, 439– 441 Austria 112, 194– 195, 312, 335, 362– 363, 374, 424, 437, 456 – 460 Bahamas 112 –113, 154 – 155, 158, 261, 334, 358, 370, 400, 435, 446– 447 Bahrain 112 –113, 140 –141, 150, 244, 333, 356– 357, 368, 392, 435, 443– 445 Bangladesh 112, 115 –117, 213, 333, 352– 353, 364, 378, 434, 438 Belgium 112, 179, 194 –196, 313, 335, 362– 363, 374 –375, 424, 437, 456– 460 Bolivia 112, 166– 168, 171, 173, 277, 334, 359– 360, 371, 407, 436, 448– 449 Bosnia – Herzegovina 106, 139 Botswana 112 –113, 174 –176, 287, 334, 361, 372 – 373, 412, 436, 450– 455 Brazil 112, 166, 168 – 169, 171, 173, 278, 334, 359, 371 – 372, 408, 436, 448– 449

Brunei 112 – 113, 118– 120, 218, 333, 353– 354, 365 –366, 380, 434, 439– 441 Bulgaria 112– 113, 132 –134, 235, 333, 355, 367, 388, 435, 442 Burkina Faso 112 –113, 174, 176 –177, 288, 334, 360– 361, 372 –373, 413, 436, 450– 455 Cameroon 112, 174, 177, 289, 334, 361, 413, 436, 450– 455 Canada 112 –113, 154– 157, 162, 262, 334, 358, 370, 400, 435, 446 –447 Chad 179 Chile 112, 166, 169, 171, 173, 279, 334, 359, 370– 371, 408, 436, 448 –449 China 112– 113, 118, 120– 122, 124, 130 –131, 207, 219, 333, 354, 365 –366, 381, 434, 439– 441 Colombia 112, 166, 169, 171, 173, 280, 334, 359, 371– 372, 409, 436, 448 –449 Congo 112 – 113, 174, 177 – 178, 290, 334, 361, 413, 436, 450 – 455 Costa Rica 112, 156– 157, 263, 334, 358, 401, 435, 446– 447 Coˆ te d’Ivoire 112 –113, 174, 176, 178 –179, 187, 291, 334, 360– 361, 414, 436, 450– 455 Croatia 106, 133, 139 Cuba 112 –113, 154, 156 –157, 264, 334, 358, 370, 401, 436, 446 –447 Cyprus 112, 114, 194, 196– 197, 201, 210, 314, 335, 362– 363, 374, 425, 437, 456– 460 Czechoslovakia 106, 134, 138

480

Country Index

Czech Republic 112– 113, 132, 134– 135, 236, 333, 355, 367, 389, 435, 442 Democratic Republic of Congo 112– 113, 174, 179– 180, 194, 292, 334, 361, 372 – 373, 414, 436, 450– 455 Denmark 112, 194, 197, 315, 335, 362– 363, 374 –375, 425, 437, 456– 460 Dominican Republic 112, 154, 157– 158, 265, 334, 358, 401, 436, 446– 447 DRC (see Democratic Republic of Congo) East Germany 106– 107 East Timor 119, 122 Ecuador 112, 166, 170 –171, 281, 334, 359– 360, 371 –372, 409, 436, 448– 449 Egypt 112, 140, 142, 144, 150, 152, 245, 333, 356 – 357, 393, 435, 443– 445 El Salvador 112, 154, 158 –159, 266, 334, 358, 370, 402, 436, 446 –447 Ethiopia 112– 113, 174, 180, 293, 334, 360– 361, 415, 436, 450– 455 Finland 112, 194, 198, 316, 335, 362– 363, 374 –375, 425, 437, 456– 460 Former Yugoslav Republic of Macedonia (see Macedonia) France 112, 132, 140, 145, 149, 151– 152, 176, 178 –179, 181– 182, 185, 188, 194 – 195, 198– 199, 208, 317, 335, 362 – 363, 374, 426, 437, 456– 460 Gabon 112 – 113, 174, 180 –181, 294, 334, 361, 415, 436, 450 –455

Germany 107, 112, 114, 136, 194–195, 199–200, 318, 335, 362–363, 374–375, 426, 437, 456–460 Ghana 112, 174, 176, 181– 182, 185, 295, 334, 361, 373, 416, 436, 450 –455 Greece 112, 194, 200 – 201, 210, 319, 335, 362– 363, 374 –375, 427, 437, 456 –460 Guatemala 112, 154, 159 – 160, 267, 334, 358, 369– 370, 402, 436, 446 –447 Guinea 112 –113, 174, 182– 184, 296, 334, 360– 361, 372 –373, 416, 436, 450 –455 Haiti 112, 154, 157– 158, 160, 268, 334, 358, 370, 403, 436, 446 –447 Honduras 112, 154, 161, 269, 334, 357 –358, 369 –370, 403, 436, 446 –447 Hong Kong 112, 118, 121– 122, 220, 333, 353– 354, 365, 381, 434, 439 –441 Hungary 112 –113, 132, 135 –136, 237, 333, 355, 367, 389, 435, 442 Iceland 112, 194, 201– 202, 320, 335, 362 –363, 374 –375, 427, 437, 456 –460 India 112, 115 –117, 184, 214, 333, 352 –353, 364, 378, 434, 438 Indonesia 112, 118, 122, 221, 333, 354, 365– 366, 381, 434, 439– 441 Iran 112, 140, 142 –144, 152, 246, 333, 357, 393, 435, 443 – 445 Iraq 112, 140, 143 –144, 146, 152, 210, 247, 333, 356– 357, 368, 393, 435, 443– 445 Ireland 112, 194– 195, 202, 205, 321, 335, 362– 363, 374, 428, 437, 456 –460 Irian Jaya (see Papua)

Country Index

Israel 112, 140, 142, 144– 147, 151– 152, 206, 248, 333, 356 – 357, 394, 435, 443 – 445 Italy 112, 194, 202 –203, 322, 335, 362– 363, 374, 428, 437, 456 – 460 Jamaica 112, 154, 161– 162, 270, 334, 357– 358, 370, 404, 436, 446 – 447 Japan 112, 118, 123 –124, 127, 129, 201, 222, 333, 354, 365 –366, 382, 434, 439– 441 Jordan 112, 140, 144 –146, 249, 333, 356– 357, 394, 435, 443– 445 Kashmir 117 Kenya 112, 174, 183, 297, 334, 360– 361, 373, 417, 436, 450 – 455 Kosovo 106, 133, 139 –140 Kuwait 1, 112, 140, 143, 146, 250, 333, 369, 395, 435, 443 –445 Lebanon 112, 140, 144, 146 – 147, 152, 184, 251, 333, 357, 395, 435, 443– 445 Liberia 112, 174, 182 –184, 298, 334, 361, 372 – 373, 417, 436, 450– 455 Libya 112, 140, 147 –148, 252, 333, 356– 357, 396, 435, 443– 445 Luxembourg 112, 114, 194 –195, 203– 204, 323, 335, 362– 363, 374, 429, 437, 456 – 460 Macedonia 106, 133, 139, 200 Malawi 112, 174, 184 – 185, 299, 335, 361, 372, 417, 436, 450 –455 Malaysia 112, 118, 123– 124, 129, 201, 223, 333, 353 – 354, 365 – 366, 382, 434, 439 – 441 Mali 112, 174, 185 –186, 300, 335, 361, 372, 418, 437, 450 –455 Malta 112, 114, 194, 204 –205, 324, 335, 362– 363, 374, 429, 437, 456– 460

481

Mexico 112, 154, 162– 163, 271, 334, 358, 370, 404, 436, 446 – 447 Mongolia 112 –113, 118, 124– 125, 224, 333, 354, 383, 434, 439 –441 Montenegro 106, 139– 140 Morocco 112, 140, 148 –149, 253, 333, 357, 368– 369, 396, 435, 443 –445 Mozambique 112 – 113, 174, 186, 301, 335, 361, 372– 373, 418, 437, 450 –455 Namibia 179 Nepal 116 The Netherlands 112, 114, 194 – 195, 205, 325, 335, 362– 363, 374, 429, 437, 456– 460 New Zealand 112, 118, 125, 225, 333, 353– 354, 365, 383, 434, 439 –441 Nicaragua 112, 154, 163, 272, 334, 358, 370, 405, 436, 446 – 447 Nigeria 112, 174, 177, 186– 187, 302, 335, 361, 419, 437, 450 – 455 North Korea 112 –113, 118, 126, 130, 143, 226, 333, 354, 365 – 366, 384, 435, 439– 441 Northern Ireland 202, 211 Norway 112, 118, 194, 206, 326, 335, 362 –363, 374 –375, 430, 437, 456 –460 Oman 112 –113, 140, 149, 254, 333, 356 –357, 368 –369, 397, 435, 443 –445 Pakistan 112, 115– 117, 215, 333, 352 –353, 364, 379, 434, 438 Panama 112, 154, 164, 273, 334, 358, 370, 405, 436, 446– 447 Papua 127 Papua New Guinea 112 –113, 118, 127, 227, 333, 354, 365 – 366, 435, 439 –441

482

Country Index

Paraguay 112, 166, 171 –172, 282, 334, 359, 371 – 372, 410, 436, 448– 449 Peru 112, 166, 171 –172, 283, 334, 359, 371– 372, 410, 436, 448 – 449 Philippines 112, 118, 128, 228, 333, 353– 354, 365, 385, 435, 439 – 441 Poland 1, 112 –113, 132, 136, 238, 333, 355, 367, 389, 435, 442 Portugal 112, 174, 186, 194, 206– 207, 327, 335, 362 – 363, 430, 437, 456– 460 Qatar 112 –113, 140, 149 –150, 255, 333, 357, 369, 397, 435, 443 –445 Rhodesia 186, 193 Romania 112 –113, 132 –133, 136– 137, 239, 333, 355, 367, 390, 435, 442 Russia 112 – 113, 125, 132, 137 –138, 240, 333, 355, 366 – 367, 390, 435, 442 Rwanda 179 Saudi Arabia 1, 112, 140, 143, 146, 150– 151, 256, 334, 357, 368 – 369, 397, 435, 443 – 445 Senegal 112, 174, 185, 187 – 188, 303, 335, 372, 419, 437, 450 –455 Senegambia 188 Serbia 106, 139– 140 Serbia and Montenegro (see also Yugoslavia) 106, 139 Sierra Leone 112 –113, 174, 182, 184, 188– 189, 304, 335, 361, 372 – 373, 420, 437, 450 – 455 Singapore 112, 118, 128– 129, 229, 333, 353– 354, 366, 385, 435, 439– 441 Slovakia (see Slovak Republic) Slovak Republic 112 – 113, 132, 136, 138– 139, 241, 333, 355, 367, 391, 435, 442 Slovenia 106, 136, 139

South Africa 112, 174, 186, 189– 190, 305, 335, 361, 372– 373, 420, 437, 450 –455 South Korea 112– 113, 118, 126, 129 –130, 230, 333, 353– 354, 365 –366, 385, 435, 439– 441 Spain 112, 168 – 169, 194, 207 – 208, 328, 335, 362– 363, 374, 431, 437, 456 –460 Sri Lanka 112, 115, 118, 206, 216, 333, 352– 353, 364, 379, 434, 438 Sudan 112, 146, 174, 179, 190, 306, 335, 361, 373, 421, 437, 450 –455 Sweden 112, 194, 208 –209, 329, 335, 362 –363, 431, 437, 456– 460 Switzerland 112, 194, 209, 330, 335, 362 –363, 374, 432, 437, 456– 460 Syria 112, 140, 144, 146– 147, 151 –152, 257, 334, 357, 398, 435, 443 –445 Taiwan 112, 118, 130, 231, 333, 353 –354, 386, 435, 439– 441 Tanzania 112, 174, 190 –191, 307, 335, 361, 372, 421, 437, 450 –455 Thailand 112, 118, 131, 232, 333, 353 –354, 365, 386, 435, 439– 441 Tibet 121 Togo 112, 174, 191 –192, 308, 335, 361, 421, 437, 450– 455 Trinidad and Tobago 112, 154, 164 –165, 274, 334, 358, 370, 405, 436, 446– 447 Tunisia 112, 140, 144, 152, 258, 334, 357, 368– 369, 398, 435, 443– 445 Turkey 112, 194, 197, 201, 209 –210, 331, 335, 362– 363, 374– 375, 432, 437, 456– 460 Turkish Republic of Northern Cyprus 198, 210 UAE (see United Arab Emirates) Uganda 112, 174, 179, 192 – 193, 309, 335, 361, 373, 422, 437, 450 –455 UK (see United Kingdom)

Country Index

United Arab Emirates 112, 140, 149, 153, 259, 334, 357, 368 –369, 399, 435, 443– 445 United Kingdom 112, 121, 125, 169, 194, 204– 205, 210– 211, 332, 335, 362– 363, 375, 433, 437, 456 – 460 Uruguay 112, 166, 172– 173, 201, 284, 334, 359 – 360, 371, 411, 436, 448– 449 USA 112, 123 –126, 129, 137, 145– 146, 148, 154 –157, 160– 166, 173, 179, 195, 197, 201, 209 –210, 275, 334, 358, 370, 406, 436, 446– 447 USSR 106 Venezuela 112, 166, 170, 173– 174, 285, 334, 359 – 360, 371– 372, 411, 436, 448– 449

483

Vietnam 112– 113, 118, 131– 132, 233, 333, 353– 354, 365– 366, 387, 435, 439– 441 Vojvodina 106 West Germany 106 – 107 Yemen 112– 113, 140, 146, 153, 260, 334, 357, 399, 435, 443 – 445 Yugoslavia 106, 112 –113, 132 –133, 139 –140, 242, 333, 354– 355, 366 –368, 391, 435, 442 Zambia 112, 174, 179, 193, 310, 335, 361, 373, 422, 437, 450 – 455 Zanzibar 191 Zimbabwe 112, 174, 179, 186, 193 –194, 311, 335, 361, 373, 423, 437, 450– 455

SUBJECT INDEX Agency country risk ratings 17 –18, 28, 93, 471 Agency data 28 Agency ratings 1, 9, 86, 102, 104, 106, 471 Amnesty International 192 ANZUS alliance 125– 126 Arab League 142, 152 Arab world 142, 149 ARCH 338– 340, 345– 347 ASEAN Asian crisis (see South East Asia crises) Asia Pacific Economic Cooperation (APEC) 122 Asymmetric effects 337– 338 Asymmetric shocks 6, 338 Asymmetry 338, 349 Asymptotic theory Asymptotic normality 337, 340 –342, 344 – 346, 352– 354, 356 –357, 360 – 362, 472 Consistency 337, 340 – 342, 344 –346, 352 – 354, 356– 357, 360 –362, 472 Auxiliary assumptions 4, 10, 24 –25 Bank for International Settlements (BIS) 11 Barron’s 106 BBC News 115 Berlin Wall 106, 199 BHHH algorithm 350 Bond issuer 95, 100 Bonds 6, 17– 18, 68, 86, 90, 95, 100, 172, 338 British mandate of Palestine 144

Business Environment Risk Intelligence S.A. (BERI) 5, 93 – 95, 97 – 105, 109, 472 Business Risk Service (BRS) 99, 109 Capital controls 3, 124 Capital markets 75, 94– 96, 99, 101 Categorical data 474 CCC 342 –343, 350, 363 Central European Free Trade Agreement 136 Central Intelligence Agency 115, 212 CFA franc 176 –178, 181, 185 Civil conflict 118, 163, 186, 190 Civil unrest (see Civil war) Civil war 3, 146, 152, 154, 158 –159, 163, 174, 178 –179, 183 –184, 188 – 190, 192, 210 Cointegration 16 Cold War 106, 144, 157, 201, 208 Communist Block 1, 4, 93, 106, 133 Communist regime 132– 136 Communist rule (see Communist regime) Communist system (see Communist regime) Comparative assessment 5, 111 –112, 211, 349, 377 Component analysis 93, 111 Composite risk 5– 7, 26, 95, 102, 104, 106, 108, 111 –112, 115 – 211, 349– 352, 354– 361, 363 – 377, 438, 441 – 442, 445, 447, 449, 454, 459, 469, 472 – 473 Conditional correlations Constant (see Static)

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486

Subject Index

Static 7, 342, 349– 350, 363– 377, 438 –439, 442 – 443, 446, 448, 450, 456, 462 –469, 473 –474 Time-varying 474 Conditional mean 6, 79, 337, 349, 377, 473 Conditional variance 6, 15, 337, 340, 342, 349, 377, 473 Conditional volatility models 1, 6, 7, 337 –338, 471 Country profiles 1, 115, 212, 472 Country risk 1 –4, 8 –11, 15 – 17, 22, 27 –31, 34, 36, 38, 40, 45, 63 –64, 66, 68 – 69, 71– 73, 87, 90 –91, 93 –96, 100 – 102, 104 –105, 107, 109 –110, 126, 130, 133, 135, 138, 151, 174, 176 –177, 197 – 198, 200, 204, 206, 211, 471 –472 Country risk literature 2 –5, 7, 9– 10, 15, 21, 25, 28, 93 – 94, 105, 471 –472, 474 Country risk ratings 1, 4 –8, 20, 25 –27, 29 –31, 35, 37, 39, 43 –44, 47, 55 –56, 63 –64, 66, 68 –69, 71 –72, 76, 79, 87, 90, 93– 100, 104 –106, 109 –112, 211, 337 –338, 345 –346, 349 – 350, 376– 377, 471 –474 Country risk returns (see Risk returns) Country Risk Service (CRS) 99, 109 Credit quality 1 –2 Credit risk 2, 17 – 18, 83, 97 Creditworthiness 29– 30, 34– 35, 44, 47, 49, 55, 68, 73, 77 –80, 83, 87, 89, 93, 95– 97, 101, 108 Crises Debt 3, 10, 17 –18, 47, 72, 78, 93, 95, 97, 163, 210 Economic and/or financial 3, 10, 84, 94, 96 –97, 122, 124 –125, 129, 134, 157, 159, 168, 170 –171, 174, 210 Russian 96, 125, 137– 138, 167

Latin/South American 3, 94, 96, 163, 167 –168, 173 South East Asian 94, 116 –117, 120 – 122, 124– 125, 128, 130 – 132, 137, 167 – 168 Cross –section data (see Data) Currency debt Foreign 96, 101 Local 100 –101 Data Cross – section 4, 10 –12, 14 –15, 28, 32, 35, 37– 40, 55, 60, 64, 66 – 67, 76, 471 Municipality 12, 14, 60 Pooled 4, 10 –13, 28, 33, 34, 36, 38, 42 –47, 49 –50, 53 – 54, 56 – 59, 61 –63, 65 –66, 68– 69, 88, 471 Time series Annual 11, 13, 43, 63, 83 Monthly 6, 13, 63 –64, 106, 350 – 351 Semi – annual 11, 13, 33, 43 – 44, 57 – 58, 63, 68, 84 Debt default 17 –18, 34, 40, 43, 49 – 50, 162 Debt indicators 28, 30, 32, 61, 66, 89, 100 Debt repudiation 2, 29, 42, 63– 64, 95 Debt rescheduling 9 –10, 17 –18, 25 – 26, 28 –31, 33 –34, 39– 40, 44 – 47, 49 –59, 61 –62, 65– 67, 69 – 71, 74, 80– 89, 91, 93, 140, 144, 178, 471, 474 Debut year 97 –98 Default risk 3, 10, 29, 42, 74, 96, 109 Degree of risk 108 Dependent variable 4, 9– 10, 17– 18, 25, 28, 32, 34, 36, 38, 40 –42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 69, 72, 94, 471 Description of risk High 107– 108

Subject Index

Low 107 – 108 Moderate 107 –108 Very high 107 –108 Very low 107 – 108 Descriptive statistics 4, 9 – 10, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 471 Diagnostics Chow test 25, 77 Exogeneity 25, 78 Heteroscedasticity 25, 72 Non – normality 25, 85 Serial correlation 25, 91 White’s standard errors 25, 74, 79, 81 WLS 25, 72 Donors Bilateral 188, 191 Multilateral 188, 191 Duff and Phelps 98 Econometric criteria 1, 3 –4, 9 –10, 28, 471 Economic conditions 3, 107, 129, 161, 178, 180 Economic explanatory variables (see Explanatory variables) Economic risk 2– 3, 29, 43, 76, 93, 95, 103 –105, 107 – 108, 111, 134, 137, 168, 200 The Economist 115, 212 Economist Intelligence Unit (EIU) 5, 17 –18, 27 –28, 47, 93 –95, 97 –105, 109, 472 Empirical findings 4, 9– 10, 21, 25, 70 –91 Enhanced Structural Adjustment Facility (ESAF) 186 Environmental performance and sustainability 16, 117, 155, 185, 474 Estimation Bayesian 24, 77 Discriminant methods 24, 73, 85, 91

487

Heckman’s two –step procedure 24, 81 OLS 22, 24, 28, 71– 74, 76 – 77, 79, 81 – 83, 87, 89 –90 ML 24, 25, 28, 70 –76, 78 –80, 82 – 83, 86 – 90 Recursive 473 Rolling 473 WLS 24 –25, 72 EU 119, 124– 125, 127, 135– 139, 145, 148, 156, 160, 162, 173, 195 – 198, 200– 201, 203– 210 Euromoney 5, 8, 11, 17– 18, 27– 28, 35, 39, 47, 63, 66, 71, 73, 93 – 95, 97 – 100, 102– 105, 109, 472 European Bank for Reconstruction and Development 140 European Monetary Union (EMU) 195, 197, 200, 207 –210 Evaluation 1, 5, 6, 8– 9, 11 28– 29, 36, 38, 73, 81, 84, 93 – 94, 96, 109, 211, 337 –338, 345, 471 –473 EViews 350 Explanatory variables 4, 9 – 10, 17 – 20, 28, 32 –69, 471 Extreme observations 158, 339, 351 Financial Executive’s Country Risk Alert 99 Financial explanatory variables (see Explanatory variables) Financial returns 6, 338, 349, 376, 473 Financial risk 2 –3, 29, 43, 76, 93, 103 – 105, 107– 108, 111, 151, 168, 200, 204 –207, 345 Fitch IBCA 5, 93– 106, 109, 472 Fitch Investor Services 98 Fitch Ratings 95, 97– 98, 100, 109 Foreign direct investment 3, 34, 38, 64, 102, 117 –118, 132, 134, 136, 167, 172, 186, 205 Foreign exchange controls 3, 117, 124 – 125, 147, 151, 175, 189, 191, 193

488

Subject Index

Foreign Investment Advisory Service Program 96, 110 Frequency of ratings Annual with monthly updates 99 Annual with weekly updates 99 Monthly 99 Quarterly 99 Quarterly with monthly updates 99 Semi – annual 99 Tri –annual 99 Functional form misspecification 15 GARCH 337 –342, 344 – 347, 349 –378, 380 – 460, 472– 474 Geographic regions (see Regions) GJR 341, 343 – 345, 349– 378, 380 –460, 472 – 474 Global QMLE 341 (see also QMLE) Gulf conflict 119, 144 –145, 151 Gulf country/nation 141, 143, 149, 153 Gulf crisis (see Gulf conflict) Gulf war (see Gulf conflict) Hedging 6– 7, 338, 350, 376, Heteroscedasticity 15, 25, 72, 337 –338, 346 – 347, 376 Highly indebted poor countries 54, 57, 99, 161, 180, 182, 184, 192 –193 HIV/AIDS 175, 185, 189, 193– 194 Intellectual property innovation and protection 116, 174, 474 Inter – American Development Bank 170 International Banking Credit Analysis Ltd. (IBCA) 98 International comparison 111, 349, 379 International Country Risk Guide (ICRG) 1, 5– 7, 27 – 28, 38, 43, 63, 93 –95, 97 –111, 212, 349, 350, 376 –377, 472 –474

International Monetary Fund (IMF) 1, 11, 30, 46– 47, 51– 52, 57 – 58, 63, 68, 78, 84, 87, 106, 109, 117, 129, 140, 142, 145, 148, 154, 161, 163, 167, 173, 175 – 177, 179, 181 – 187, 190 – 192, 194, 210 Institutional Investor (II) 5, 11, 17– 18, 27 – 28, 59, 63– 64, 68– 69, 71, 73, 87 –88, 90 –91, 93 – 95, 97 – 100, 102– 105, 109– 110, 472 Investor confidence 124, 137, 158 – 159, 161– 162, 180, 191 Iran – Iraq war 152 Israeli –Palestinian conflict 142, 145, 151 Issuer 95, 100– 101 Journal/source 4, 9– 10, 32– 69 Kurtosis 16, 339 Leverage effects 6, 338 Likelihood function 340, 344 Linear regression model (see Model specification) Local QMLE 341 (see also QMLE) Log – linear regression model (see Model specification) Log – moment condition 340 – 342, 344 – 345, 351– 362, 377 – 434, 473 Maastricht Treaty 197, 199, 200 Madrid coference 144 Marquardt algorithm 350 Mercosur 173 Model specification Artificial neural network 16– 17, 29, 39, 72 Discriminant 15 –16, 24, 28, 73, 77, 86, 89 –91, 472 Linear regression model 15– 17, 20 – 22, 24, 28, 72– 74, 76– 77, 79, 81, 83 –84, 89 –91

Subject Index

Log –linear regression model 15 –17, 24, 28, 72, 76, 90 Logit 15– 17, 24– 25, 28 –30, 33, 50, 70 –74, 76 – 78, 80, 82– 84, 86 –87, 89 –90, 471 Probit 15 – 16, 24– 25, 28– 29, 34, 59, 71, 73– 74, 78– 81, 84, 87 –88, 471 Systems 16, 74 –75, 78, 86 Tobit 16, 24, 28, 74, 81, 84 Moment conditions Second moment 339– 342, 344 –345, 351 – 362, 377 –434, 473 Third moment 339 Fourth moment 339 – 342, 344, 346 Sixth moment 341 Eight moment 342 Moments 16, 337 – 342, 344, 346 –347, 472 Moody’s 5, 11, 17 – 18, 27– 28, 30, 37, 60, 72, 86, 93– 100, 102– 105, 109 –110, 472 Moody’s Investors Service 96, 100, 110 Multivariate models 1, 6, 337– 347, 473 –474 NAFTA 155 NATO 135– 136, 138– 140, 208 –210 Non – normality 16, 25, 85, 337, 345, 473 Normality 340 OECD 34, 46, 119,139, 175 Omitted explanatory variables 4, 9 –10, 20 –23, 28, 70 –91 OPEC 150 Ordered models 474 Outliers 85, 339, 351 Panama Canal 164 Palestine Liberation Organization (PLO) 146, 206

489

Paris Club 140, 148, 168, 181,187, 192 Political explanatory variables (see Explanatory variables) Political risk 2 – 3, 8, 29, 43, 71, 76, 93, 100, 102 –105, 107 –108, 110 – 111, 125, 134 – 135, 138 – 139, 151, 155, 160, 183 – 185, 187, 189 – 190, 193, 196 – 198, 205 Political Risk Services (PRS) 5, 27 – 28, 64, 93 –95, 97 – 100, 102 – 105, 109, 472 Pooled data (see Data) Poor’s Publishing 97 Preferred model 28, 351 –363, 434 – 437 Preferred stock 100– 101 Pricing risk ratings 6, 338 Privatization 116, 118, 129, 134– 135, 137 – 138, 142– 143, 145, 148, 158, 167, 170, 173, 176– 178, 180, 183, 185, 189, 190 Probability of debt rescheduling (see Debt rescheduling) Proxy variables 4, 10, 17 – 18, 20– 23, 28, 30, 34 – 38, 48– 50, 53– 57, 60 – 64, 69, 83 The PRS Group 97, 110, 212, 335 QMLE 339 –342, 344 – 345, 351– 362, 377, 473 Qualitative comparison 5, 27, 93– 95, 109, 472 Quantitative rating systems 3, 5, 8, 10, 31, 93 –94, 105 Rankings 90, 109– 110, 375– 377, 469, 473 R&A (see S.J. Rundt and Associates (R&A)) Rates of change 6, 338, 349, 376, 473 Rating systems 1, 4 – 5, 7, 27, 93– 94, 96, 103 –104, 109, 471 –472, 474

490

Subject Index

Ratings (see Risk ratings) Refugees 3, 159, 189 Regional differences 18– 20, 44, 48, 56, 60 –61, 77 Regions Central and South Asia 5, 112– 113, 115, 333, 351 –353, 363 –364, 375 –376, 378 – 379, 434, 438, 461 –462, 469 East Asia and the Pacific 5, 112 –113, 118, 333, 351, 353 –354, 363, 365, 375 –376, 380 –387, 434, 438 –441, 461 –463, 469 East Europe 5, 112 – 113, 132, 333, 351, 354– 356, 363, 366– 367, 375 –376, 388 –391, 434 –435, 442, 461, 463 –464, 469 Middle East and North Africa 5, 112 –113, 140, 333 –334, 351, 356 –357, 363, 368, 375 –376, 392 –399, 434 – 435, 443– 445, 461, 464 –465, 469 North and Central America 5, 112 –113, 154, 334, 351, 357 –358, 363, 369, 375 –376, 400 –406, 434 – 436, 446– 447, 461, 465 –466, 469 South America 5, 112– 113, 166, 334, 351, 359, 363, 370, 371, 375 –376, 407 –411, 434, 436, 448 –449, 461, 466 –467, 469 Sub –Saharan Africa 5, 112 –113, 174, 334 –335, 351, 372, 375 –376, 412 – 423, 434, 436 –437, 450 – 455, 461, 467 –469 West Europe 5, 112– 113, 194, 335, 351, 361 –364, 373 –377, 424 –434, 437, 456 –461, 468 –469 Regularity conditions Necessary 340, 341, 347

Sufficient 337, 339 – 341, 344 – 345, 347, 377, 472 –473 Representative countries (see Country Index) Returns (see Risk returns) Risk analysis 3 –4, 6, 8, 10, 31, 94, 110, 337, 471 Risk component variables 5, 9, 26– 27, 94, 102 –105, 108 – 109, 472, 474 Risk free measures 107 Risk indicators 3, 10, 19, 68– 69, 71, 87, 93, 100– 102, 104, 109, Riskiness 1, 471 Risk rating agencies 1, 4 – 5, 27, 72, 79, 86, 93– 111, 472, 474 Risk rating industry 94– 95, 97 Risk rating methodologies 96, 109 – 110 Risk ratings Forecasts 97, 100 –102, 104, Grading range Letter gradings 105 Numerical gradings 105 Long –term 100– 101, 104 Risk points 107 –108 Short – term 100 –101 Risk returns 1, 5– 7, 111 –112, 114 – 211, 213– 332, 337– 339, 342 – 343, 345, 349 – 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 400, 402, 404, 406 –408, 410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 438– 452, 454, 456 –459, 461 – 469, 471 – 474 Risk return shocks 1, 7, 343, 345, 349, 350, 363, 376, 377, 473 Risk spillovers 6, 70, 337, 342– 343 Rundt, S.J. and Associates (R&A) 5, 93 – 95, 97 – 105, 109 – 110, 472

Subject Index

Securities 95, 100– 101 September 11, 2001 4, 94, 119, 138, 142, 148, 155 –157, 162 –163, 166, 197, 203 Serial correlation 16, 25, 91 Shocks Long –run persistence 340, 345 Short –run persistence 345, 352 –353, 355 – 356, 358– 360, 362 Standardized 337, 340, 345, 349, 351, 363, 377, 473 Unconditional 338 Sovereign government 2 – 3, 95 – 96, 101, 104, 109 Sovereign risk 2, 101 Sovereign state (see Sovereign government) Soviet republics 106 Soviet Union 124, 126, 135, 137– 138, 144, 151, 157 Spillovers 3, 70, 173 Standard and Poor’s (S&P) 5, 11, 17 –18, 27 –28, 30, 37, 60, 72, 86, 93 –95, 97 –105, 109 –110, 472 Standard Statistics 97 Stationary processes 15, 340, 347 Statistical criteria 1, 3– 4, 9 – 10, 28, 471 Statistical properties (see Asymptotic theory) Structural breakpoints 474 Structural change 16, 106, 119, 125, 130, 134, 140 –141, 144 –146, 150 –152, 155 – 156, 163, 166, 195, 197 –200, 202 –203, 205, 207, 210 Structural properties 337, 342 Suez Canal 142 “Sunshine” policy 126, 130 Symmetry 16, 349, 377, 473 Testing 6, 9, 15, 28, 30, 60, 78, 338, 345, 347, 371 –372, 374

491

Thick – tailed distributions 338 Third World debt crisis 95, 97 Threshold effects 338 Time series data (see Data) Trends 5, 54, 111 –112, 115 – 211, 472 Types of variables 4 – 5, 10, 17 –18, 21 – 23, 25 – 26, 28, 94, 103, 109, 471 –472 UK Trade and Investment 115, 212 Unit roots 16 United Nations (UN) 33, 123, 130, 138 – 139, 143– 144, 146, 148, 184, 189, 192, 201, 209 – 210 Univariate models 1, 6, 7, 16, 337 – 339, 343– 345, 349– 363, 377 – 437, 471– 474 Uruguay Round agreement 201 US Department of State 115 VARMA –AGARCH 337, 339, 341 – 343, 345, 363, 472, 474 VARMA –GARCH 337, 339, 341 – 342, 344– 345, 363, 472, 474 “Velvet” divorce 134, 138 Volatility Clusters 116 – 117, 119, 121, 125, 127 –128, 130, 132, 134 – 138, 143– 144, 149, 153, 155 –157, 160 – 162, 164, 166, 169, 173, 176– 177, 181 – 183, 185, 187, 190 –195, 197 – 198, 200– 203, 206, 208, 211, 338 Conditional 1, 6 –7, 15, 337– 339, 345, 349, 471 –472, 474 Definition 115 Multivariate 337– 338, 346 Persistence 338, 345 Stochastic 338 –339, 474

492

Subject Index

Time – varying 6, 7, 338– 339, 349 Univariate 338, 345

Wall Street Journal 106 Weak exogeneity 16 Weights for risk components 5, 94, 104 –105, 109, 472

West African Economic and Monetary Union 178, 185, 187 World Bank 1, 11, 96, 106, 140, 148, 152, 157, 170, 176 –177, 182 – 185, 187, 191 – 192 WTO 121– 122, 130, 145 WWI 97, 145, 204, 209 WWII 123, 127, 202, 204– 205, 209– 210