Developing an early warning system to predict currency crises in emerging markets

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DEVELOPING AN EARLY WARNING SYSTEM TO PREDICT CURRENCY CRISES IN EMERGING MARKETS MASTER OF ARTS IN DEVELOPMENT ECONOMICS By HOANG THUY HONG NHUNG Academic Supervisor Assoc Prof NGUYEN VAN NGAI Ho Chi Minh City, December 2014 CERTIFICATION “I certify that the substance of this thesis has not already been submitted for any degree and has not been currently submitted for any other degree I certify that to the best of my knowledge and help received in preparing this thesis and all used sources have acknowledged in this dissertation” HOANG THUY HONG NHUNG th Date: 27 December 2014 i ACKNOWLEDGEMENTS Upon completing this thesis, I have received a great deal of encouragement and support from many people First of all, I would like to express my deep gratitude to Assoc Prof Nguyen Van Ngai, my academic supervisor, for his patient guidance, enthusiasm and encouragement I would also like to thank Dr Truong Dang Thuy for his professional advices, and Mr Truong Hong Tuan and Mr Luong Duy Quang, former students, for their valuable comments My gratefulness is also extended to all of my lecturers and staff of the VietnamNetherlands Program, particularly, Assoc Prof Nguyen Trong Hoai and Dr Pham Khanh Nam for their assistance during the first days when I started this program I wish to thank my family for their encouragement and support during my study as well Without them, I would not have a chance to finish the thesis Finally, I would like to thank all my friends and other people who have had any help and support for my thesis but are not above-mentioned ii ABSTRACT This thesis develops a new early warning system (EWS) model to predict the currency crises in emerging markets by using the logit regression According to the results, the macroeconomic variables and the institution variables are valuable indicators which play important roles in EWS model for predicting the currency crises It shows that the real exchange rate, export growth, import growth, current account surplus/GDP, short-term debt/reserves have correct sign and are statistically significant at 5% level It also shows that the law and order, external conflict have correct sign and are statistically significant at 1% In addition, this thesis also applies credit-scoring method to get the optimal cut-off threshold in order to have a more accurate probability of predicting currency crises Since then, the policymakers can consider taking the effective pre-emptive actions to prevent the currency crises occurring in the future Key words: currency crisis, early warning system, emerging market, logit model TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Research questions 1.4 The scope of the thesis 1.5 The structure of the thesis CHAPTER 2: LITERATURE REVIEWS 2.1 Definition of currency crisis 2.2 Theoretical literatures of currency crises 2.2.1 First generation models of currency crises 2.2.2 Second generation currency crisis theoretical model 2.2.3 Third generation currency crisis theoretical model 10 2.2.4 “Fourth generation” currency crisis theoretical model 12 2.3 Empirical studies of currency crises 14 2.3.1 Indicators of currency crisis 14 2.3.2 Existing methods approach in EWS model of currency crisis .16 2.3.3 Summary of recent empirical findings 19 2.4 Conceptual framework 26 CHAPTER 3: RESEARCH METHODOLOGY AND DATA 28 3.1 The EWS model specification .28 3.1.1 Dating the currency crisis and define the dependent variable 28 3.1.2 Explanation variables choice and hypothesis testing 29 3.1.3 Methodology research 36 3.2 How to choose the optimal cut-off threshold 39 3.3 Data collection .42 3.4 Estimation strategy and statistical tests of the model 43 CHAPTER 4: RESEARCH RESULTS 45 4.1 The descriptive statistic of the sample 45 4.2 Empirical results 50 4.2.1 Effected by macroeconomics factors 51 4.2.2 Effected by institution factors 53 4.3 Choosing the optimal cut-off threshold 55 4.4 Predicting the currency crisis 58 4.4.1 Asian Crisis 1997-1998 .59 4.4.2 Turkey crisis in 1994 and 2001 59 4.5 Robustness test 62 4.5.1 Out-of-sample test of Latin America case 62 4.5.2 Choosing optimal cut-off threshold of EWS model in Latin American .64 4.6 Compare results with other empirical studies 65 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS .68 5.1 Main findings 68 5.2 Policy implications 69 5.3 Research limitations 71 5.4 Suggestions for future researches 72 REFERENCES 73 APPENDIX A: LITERATURE WORKSHEET AND DATA SOURCES .77 APPENDIX B: RESULTS OF CHOOSING CUT-OFF THRESHOLDS AND PREDICTING VALUE OF EWS MODEL IN ASIA 90 APPENDIX C: RESULTS OF ROBUSTNESS TEST 93 APPENDIX D: DISCRIPTIVE STATISTIC 96 APPENDIX E: COMPARISON OF TWO MODELS: MACROECONOMIC VARIABLES ONLY AND INCLUDING INSTITUTIONS VARIABLES 108 LIST OF FIGURES Figure 2.1: The flowchart of developing an EWS model to predict currency crises 19 Figure 2.2: Conceptual framework 27 Figure 3.1: Logit and probit cumulative distributions .37 Figure 3.2: The optimal cut-off identification 42 Figure 3.3: Research processing 44 Figure 4.1: Optimal cut-off threshold of 12-months EWS model in Asian countries 57 Figure B.1: The fitted and predicted value of EWS model in Asian countries 90 Figure C.1: Optimal cut-off threshold of 12-months EWS model in Latin America 93 Figure C.2: The fitted and predicted value of EWS model in Latin America countries .94 Figure D.1: Reserve loss 96 Figure D.2: Export growth 96 Figure D.3: Import growth 98 Figure D.4: Short-term debt/Reserves 99 Figure D.5: GDP growth 100 Figure D.6: Current account/GDP 101 Figure D.7: Real exchange rate growth 102 Figure D.8: Government stability 103 Figure D.9: Corruption 104 Figure D.10: Law and order 105 Figure D.11: External conflict 106 Figure D.12: Internal conflict 107 LIST OF TABLES Table 3.1: Summary expected sign of explanation variables 36 Table 4.1: The summary of sample used in the regressions 47 Table 4.2: The multicollinearity between independent variables 48 Table 4.3: The correlation between independent variables 49 Table 4.4: The empirical results of logit regression of 12-month EWS model 51 Table 4.5: Specification error test 51 Table 4.6: Probability of predictability of 12-months EWS model (cut-off =13.27%) .57 Table 4.7: EWS model performance with different cut-off point 58 Table 4.8: Robustness test of Asian countries in 1994, 1997, 2001, 2007 60 Table 4.9: Performance of EWS model in Asian countries when cut-off = 13.27% 62 Table 4.10: The results of EWS model in Latin American countries 63 Table 4.11: Results of explanation variables compare with other empirical studies 67 Table A.1: The summary references of explanatory variables of the model .77 Table A.2: Summary data, sources and period time of explanation variables 79 Table A.3: The literature worksheets of empirical studies 80 Table B.1: Identify optimal cut-off in Asian countries by Credit-scoring approach 90 Table C.1: Probability of predictability of 12-months EWS model (cut-off =12.02%) 93 Table C.2: Performance of EWS model in Latin American countries, cut-off = 12.02% 93 Table E.1: Comparing 12-month EWS predicting of 02 models in Asia: 1992 - 2011 .109 Table E.2: Nested model test 110 Table E.3: Specification test of macroeconomic model 110 Table E.4: Specification test of full model 110 vii CHAPTER 1: INTRODUCTION 1.1 Problem statement There were a lot of financial crises which occurred during the 1990s: the crises of European in 1992-1993, Mexico in 1994-1995, the crises of Asia in 1997-1998, Brazil in 1999, Turkey in 2001, Argentina in 2002 and the economic crises over the world in 2008-2009 These financial crises have strong influences on economy, politics and society They caused the economic uncertainty which suffered from high inflation, slow growth, high unemployment and poverty It made the GDP growth rate is negative, the abrupt changes in nominal exchange rate with over 50% devaluation In Argentina, it lost 20% of GDP growth and the real wages decrease match with it percentage The policy-makers were all under the pressure of implementing new policies in order to recover the affected economy Moreover, the cost of crises was very high, which led to an increase in the number of empirical studies with the aim of constructing the monitoring tools to predict the crisis occurrence These studies were often called early warning system (EWS) There are three common types of financial crises: currency crisis, banking crisis and debt crisis However, The EWS model in this thesis only focuses on the currency crises like most of EWS models in previous empirical studies EWS models for currency crises were first built by Krugman (1979) and enhanced by Flood and Garber (1984) They proved that reserve loss is an important indicator to predict crises Obstfeld (1994, 1996) has proposed a different model for predicting currency crises He stated that the currency crises occurred due to the expectation of speculators However, the model failed to take time matter into account therefore it could not predict the time when crises occurred After Asian crisis in 1997, it has created the foundation to develop a new model for currency crises Kaminsky and Reinhart (1999) built the models of the EWS for twin crises that combine banking crises and currency crises They also stated that, banking crisis often occurred prior to currency crisis, when the currency crisis occurred, this deepened the banking crisis; as the result the economy is in twin crises In the general, these studies used the macroeconomic and financial indicators to predict the currency crises such as foreign reserves, export and import, real interest rate, real exchange rate, M2/reserves, M2 multiplier, current account deficit (or surplus) to GDP ratio, short-term debt/reserve (Kaminsky et al.,1998, Frankel and Rose, 1996, Berg and Pattilo, 1999) In the recent years, some economists concerned about institutional factors such as bureaucratic quality, government stability, government effectiveness, voice and accountability, rules of law, democracy, election, control of corruption and so on (Block, 2003, Shimpalee and Breuer, 2006, Leblang and Satyanath, 2008) that were used to predict the probability of imminent crises Besides selecting the potential indicators, several methods have been suggested The most popular and suitable one is logit models that were applied by Frankel and Rose (1996), Berg and Pattillo (1999) And the second is the signal approaches that were proposed by Kaminsky et al (1998) and applied by Edison (2003), Bruggemann and Linne (2000), Subbaraman, Jones and Shiraishi (2003)) Some alternative approaches are cross-country regression models which proposed by Sachs et al (1996), Ordinary least square (OLS) method such as Tornell (1999), Brussiere and Mulder (1999), Markov-switching method applied by Martinez-Peria (1999), Abiad (2003), and Artificial Neural networks (ANN) method applied by Nag and Mitra (1999) Nevertheless, most of the EWS models only focus on identifying the indicators, which are statistically and economically significant, that should be included in the models to predict the currency crises, the problem raised is that the ability to predict of those EWS models were unexamined In order to solve the problem, the optimal cut-off threshold is chosen to evaluate the EWS model and minimizing the crisis risk If the chosen cut-off point is low, it will give more signals of crises, therefore APPENDIX D: DISCRIPTIVE STATISTIC Figure D.1: Reserve loss Figure D.2: Export growth Figure D.3: Import growth Figure D.4: Short-term debt/Reserves Figure D.5: GDP growth 100 Figure D.6: Current account/GDP 110 Figure D.7: Real exchange rate growth Figure D.8: Government stability Figure D.9: Corruption Figure D.10: Law and order Figure D.11: External conflict Figure D.12: Internal conflict APPENDIX E: COMPARISON OF TWO MODELS: MACROECONOMIC VARIABLES ONLY AND INCLUDING INSTITUTIONS VARIABLES The first column presented the result of Model 1, it stated that all macroeconomic variables have the significant at 1% level, except export growth at 5% level and real exchange rate has not significant Besides that, the export growth got the incorrect sign while others have the correct sign The second column showed the result of Model 2, which included macroeconomic and institution variables The export growth changes the sign from positive to negative and get the correct sign as expected and got significant at 5% level The real exchange rate growth changed from not significant (p=0.650) to significant at 1% level The other macroeconomic variables have correct sign and significant at 1% level, except reserve loss which lost a bit in their significant, from p=0.000 to p=0.108 Meanwhile all institutions have significant at 1% level; however, there is law and order and external conflict have the correct sign as expected Moreover, Pseudo R-squared of Model (=0.7312) is higher than Pseudo R-squared of Model (= 0.4192) and all of them are significant It could be concluded that the institution variables subsidize to the macroeconomic variables, it made them be better Table E.1: Comparing 12-month EWS predicting of 02 models in Asia: 1992 - 2011 Indicators Model (7 variables) Coefficient P value RESERVE EXPORT IMPORT RER CAGDP STDRES GDP GOVERNMENT CORRUPTION LAW EXTERNAL INTERNAL _CONS 0285792 0098407 0290814 0004584 -.1409066 0297002 -.2810559 0.000 0.003 0.000 0.650 0.000 0.000 0.000 -4.273266 0.000 Pseuso R2 0.4197 Model (12 variables) Coefficient P value 0171211 -.0208022 0612803 0128762 -.4071764 0689924 -.665357 1.695356 1.116541 -2.122407 -.9823548 2.393928 -35.9565 0.108 0.026 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.7312 Regression result of coefficients: - Model 1: with macroeconomic variables - Model 2: with 12 variables in which macroeconomic variables and institution variables Sources: Author’s calculation To test whether the Model and Model is better, this thesis used the nested model test The Table E.2 stated LR chi2 = 242.72 and significant, it meant that it fail to reject the null so it is should not to drop the institution variables, in another way, the Model is better than Model Besides that, in the Table E.3, the significant of _hat (p = 0.000) showed that the Model included the meaningful indicators while _hatsq also significant stated that this model has the specification error However, the Table E.4 stated that the Model did not have specification error (_hatsq, p = 0.760) and it included the good indicators (_hat, p= 0.000) It could be concluded that, the institution has the good role of preceding the currency crisis in Asian country Table E.2: Nested model test Likelihood-ratio test LR chi2(5) = 242.72 Assumption: Table E.3: Specification test of macroeconomic model Coefficient Std Err z P value _hat 1.254502 138507 9.06 0.000 _hatsq 0703091 0271464 2.59 0.010 Table E.4: Specification test of full model _hat _hatsq Coefficient Std Err z P value 1.019015 0094695 1183753 0310432 8.61 0.31 0.000 0.760 ... depression the financial crises According to Kaminsky and Reinhart (1999), the models of this period are the early warning system for “twin crises? ?? that combine banking crises and currency crises They... after the wave of financial crises in the 1990s and succeeded in determining the indicators that lead to the currency crises as well as constructing to the EWS models for preceding currency crisis... new model for currency crises Kaminsky and Reinhart (1999) built the models of the EWS for twin crises that combine banking crises and currency crises They also stated that, banking crisis often