UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MACROECONOMIC DETERMINANTS OF CREDIT RISK IN THE ASEAN BANKING SYSTEM BY NGUYEN CHI THANH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2016 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MACRO ECONOMIC DETERMINANTS OF CREDIT RISK IN THE ASEAN BANKING SYSTEM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN CHI THANH Academic Supervisor: DR NGUYEN VU HONG THAI HO CHI MINH CITY, DECEMBER 2016 DECLARATION I declare that the wholly and mainly contents and the work presented in this thesis (Macro Economic Determinants of Credit risk in the ASEAN Banking System) are conducted by myself The work is based on my academic knowledge as well as my review of others’ works and resources, which is always given and mentioned in the reference lists This thesis has not been previously submitted for any degree or presented to any academic board and has not been published to any sources I am hereby responsible for this thesis, the work and the results of my own original research NGUYEN CHI THANH i ACKNOWLEDGEMENT Here I would like to show my sincere expression of gratitude to thank my supervisor, Dr Nguyen Vu Hong Thai for his dedicated guideline, understanding and supports during the making of this thesis His precious academic knowledge and ideas has motivated me for completing this thesis Besides, I would like to express my appreciation to the lecturers and staff of the Vietnam – Netherlands Program at University of Economics Ho Chi Minh city for their willingness and priceless time to assist and give me opportunity for this thesis completion Next, I would like to thank all of my classmates for their encouragement and their hard work, which become a good example for me to the thesis I wish all of us will graduate at the same date Lastly, I would like to express my love to my families for their unlimited supports which has led to the completion of this course research project ii ABBREVIATION ASEAN: Association of Southeast Asian Nations DGMM: the difference generalized method of the moments estimator FE & RE: Fixed-effect and Random-effect estimator GDP: Gross domestic product NPLs: Non-performing loans OECD: Organization for Economic Cooperation and Development OLS: Ordinary Least Square SGMM: the system generalized method of the moments estimator iii ABSTRACT The impact of credit risk, which is caused by the increase in the non-performing loans (NPLs), on the performance and stability of banking system as well as economic activities have recently raised many interests from researchers and policy makers Motivated by the close connection between the NPLs and macroeconomic environments as proposed by many researchers, this paper will empirically examine the determinants of non-performing loans in commercial banking systems of the five ASEAN countries in the period of 2002 to 2015 The research uses a sample of 162 banks in these countries with 11 variables of macroeconomic and bank-specific factors and applies the System Generalized Method of Moments estimator (SGMM) for dynamic panel models The empirical results in this paper indicate that the movement of NPLs in the commercial banks of the five studied countries is associated with both macroeconomic variables and bank-specific factors For the macroeconomic condition, an increase in unemployment rate and the appreciation of domestic currency are found to significantly increase the NPLs In addition, bank with higher returns on asset and leverage ratio and low ratio of equity to total assets will have lower rate of NPLs Moreover, with the application of additional statistical analyses, the results indicate that the findings of the main model of this paper are consistent and robust iv CONTENTS DECLARATION i ACKNOWLEDGEMENT .ii ABBREVIATION iii CONTENTS v APPENDIX LIST OF TABLES CHAPTER 1: OVERVIEW OF RESEARCH Introduction: 1.1 Backgrounds: 1.2 Problem statements: 1.3 Research objectives: 1.4 Research questions: 1.5 Hypothesis of the study: 1.6 The importance of research: 1.7 Structure of Research: CHAPTER 2: LITERATURE REVIEWS 2.1 Theoretical reviews: 2.2 Empirical reviews: 13 2.3 Conclusion: 22 2.4 Research Hypothesis: 23 CHAPTER 3: DATA AND METHODOLOGY 27 3.1 Data collection: 27 3.2 Econometric methodology – The NPLs measurement: 28 3.3 The variables definition and measurement: 32 v 3.3.1 The dependent variable – the Non-performing loans: 32 3.3.2 Macroeconomic variables: 32 3.3.3 Microeconomic variables – bank-specific determinants: 34 3.4 Econometric strategy – The system GMM estimator: 38 CHAPTER 4: RESULTS AND DISCUSSIONs 40 4.1 Summary statistics: 40 4.2 Unit root tests: 41 4.3 Empirical results: 41 CHAPTER 5: OTHER ANALYSIS AND ROBUSTNESS CHECK 51 CHAPTER 6: CONCLUSION, POLICY IMPLICATIONS & LIMITATIONS OF THE REASEARCH 56 6.1 Main findings: 56 6.2 Policy implications: 57 6.3 Limitations: 58 6.4 Future research recommendation: 58 REFERENCES 59 APPENDIX 66 vi APPENDIX Appendix 1: Number of banks in each country Appendix 2: xtabond2 model selection criteria Appendix 3: Correlation of variables Appendix 4: Additional analyses and Robustness checks Appendix 5: Additional analyses and Robustness checks AP Page | LIST OF TABLES Table 1: Description of variables Table 2: Summary statistics Table 3: Unit root tests for NPLs estimations variables Table 4: Results with SGMM and fixed-effect estimations Page | CHAPTER 6: CONCLUSION, POLICY IMPLICATIONS & LIMITATIONS OF THE REASEARCH 6.1 Main findings: The recent problems of financial sector all around the world, especially the banking system, have deteriorated their asset quality and capital as well as an increase in possibility of loss This problem causes many interests of researchers and policy makers in the analysis of the banking crises and the causes for these Mostly the banking crises come from the credit risk, which easily leads banks to illiquidity and insolvency problems The increase of the credit risk is associated with the raise in non-performing loans in bank balance sheet, when the borrowers cannot afford or loss their ability to pay back their loans Thus, using a variety of panel estimation techniques, many researches have paid their attentions to the macroeconomic environment, which is considered as the main determinants of the raise of NPLs Following this, the objectives of this paper is to examine the effects of the macroeconomic determinants on the NPLs of commercial banks in the five ASEAN countries from 2002 to 2015 In addition, applying the dynamic panel data methodology, which in this case is the SGMM estimator, the several bank-specific factors is also investigated in order to study the nature of risk management in these bank toward the NPLs rate Besides, several additional analysis and robustness tests with different approaches are conducted in order to confirm these consistence of results in the SGMM estimator The main results of this paper still remain robust and subject to the objectives, although some of alternative examination and time-period restriction are applied and they also open the other views for this paper As can be seen from the results, some of the macroeconomic factors, especially the unemployment rate and the exchange rate, have a strong impact to the movements of NPLs in the commercial banks of the five ASEAN countries The NPLs rate increase as the appreciation of domestic currency as well as the increase in unemployment rate, which indicate for economic downturn Due to small sample data with short-term period, other variables such as the real GDP growth, real interest rate and the inflation not show any effect to the NPLs rates The unexpected positive sign of the real GDP growth indicates that the banking systems in the studied Page | 56 countries have an awareness to control their lending activities based on the movement of the business cycle In addition, for bank-specific determinants of credit risk, the results suggest an additional explanatory power in the paper that the banking system of these countries is significantly associated with the solvency ratio, the leverage and the returns of bank total assets during both the pre-crisis and post-crisis periods The findings support to the “moral hazard” and “bad management” hypotheses, which shows the connection of these indicators to the bank management quality toward the bank’s asset quality as well as the NPLs in bank balance sheets Higher the bank’s management quality with limited moral hazard incentives leads to lower NPLs 6.2 Policy implications: As indicated by the results, these variables can serve as warning indicators for the bank credit risk in the future, which can generate several policy implications for both bank managers and country regulators For the country level, a requirement of a suitable policy needs to be considered for the favorable and stable economic environment in order to ensure the stability of banking system For example, the price stability from the monetary policy is adapted for a suitable interest rate and inflation In addition, it is a fundamental demand for a structural approaches to increase productivity, support steady growth and employment as well as develop external competitiveness of country Lastly, regulatory authorities in the five ASEAN countries should strengthen supervision and regulation in financial sector for risk management systems The country needs to control banking managerial performance and to identify banks with high probability of impaired loans and financial instability so that the country can employ an in-time method to prevent from the mass collapse of the banking system in the future In case of credit risk, policymakers can establish a more proactive approach such as disposing tax and legal impediments so that banks can improve the process of their portfolios management and increase banks’ ability to absorb losses For the bank level, increase the management quality as well as efforts in credit risk management is the prior objective, banks can encourage the efficiency in credit risk analysis and loan monitoring, thus they can recognize the potential increase in bad Page | 57 quality assets In addition, it is recommended that banks should limit excessive lending as well as loans denominated in foreign currency and maintain high credit and capital standards 6.3 Limitations: During the making of this paper, several of limitations are inevitable Despite the contributions of this study, the small sample size with a limit of time span appears to be the first limitation The annual data seem to be not enough for the paper to make a more accurate and precise results rather than quarterly data or monthly data With a larger sample size, it could increase the degrees of freedom so that the more comprehensive and reliable results can easily obtain more information of banks’ immediate responses to short-term monetary policy as the rapid changes in interest rate In addition, this paper does not consider the difference of country institutions, which could significant affect to the movement of the banks’ credit risk Lastly, this paper only investigates the commercial banks in general, thus some distinctive aspects of bank credit risk determinants is not particularly studied in the case of private-banks and state-owned banks 6.4 Future research recommendation: For the future research information, as mentioned above, it is much better to enlarge the sample size for a more reliable results Furthermore, it is worth to consider a research of credit risk determinants with other statistical analytical tools such as structural equation modeling or a research on potential nonlinear credit market friction impacts In addition, the future research can extend to country regulatory, institutional and legal determinants or a comparative analysis on factors of NPLs in a particular loan types (business, mortgage and consumer loans) could be interesting for a further academic works in the case of five ASEAN countries Lastly, an investigation on particular type of banks (such as private banks and stateowned banks) will give more precise results on the bank credit risk factors, thus it will help policy makers pay attentions and manage the credit risk more accurately and appropriately for each type of banks Page | 58 REFERENCES Anastasiou, D., Louri, H., Tsionas, M (2016) Determinants of non-performing loans: Evidence from Euro-area countries Finance Research Letters http://dx.doi.org/10.1016/j.frl.2016.04.008 Asian Development Bank (2015) Asian Development Outlook 2015 – Financing Asia’s Future Growth Ahmad, N., Ariff, M., (2007) 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Roodman, 2009; Arellano & Bond, 1991 Page | 66 APPENDIX Correlation NPL SVCR DEBT INEFF LOGSIZE NOINTINC ROA UNEMP RINT REER GDPG NPL 1.0000 SVCR 0.0353 1.0000 DEBT 0.0334 0.9662 1.0000 INEFF 0.1353 -0.1233 -0.0950 1.0000 LOGSIZE 0.0228 -0.4342 -0.3636 -0.1384 1.0000 NOINTINC 0.1235 -0.0361 -0.0384 0.1424 0.1461 1.0000 -0.2017 0.2285 0.1753 -0.4678 -0.0232 0.0874 1.0000 UNEMP 0.0734 0.0250 -0.0076 0.1780 -0.3636 -0.0083 0.1818 1.0000 RINT 0.0488 0.0424 0.0322 0.0800 -0.0568 0.0160 0.0141 0.1580 1.0000 REER -0.2705 -0.0293 -0.0194 -0.0337 0.2032 -0.0733 -0.0923 -0.3603 -0.0715 1.0000 GDPG 0.0008 -0.0504 -0.0491 -0.0004 -0.1201 0.0022 0.0521 0.1180 -0.3303 0.0706 1.0000 -0.1192 -0.0242 -0.0251 0.0079 -0.2542 -0.1739 0.1016 0.2952 -0.6768 -0.0622 0.3395 ROA INFGDP INFGDP 1.0000 Page | 67 APPENDIX Additional analyses and Robustness checks Variables Constant Lagged NPLs SVCR LEVER INEFF NOINTINC LOGSIZE ROA UNEMP RINT INTSP REER L.GDPG L.M2 Interest rate spread (5) -10.5152 -11.3040 (11.0380) (11.7284) 0.5715*** 0.5385*** (0 0713) (0.0823) Bank-specifics 0.5412** 0.5168* (0.2723) (0.2772) -0.2167* -0.2033* (0.1113) (0.1188) -0.0040 -0.0024 (0.0355) (0.0277) -0.0326 -0.0090 (0.1138) (0.0839) 1.1503 1.1340 (0.8109) (0.7988) -2.0048** -2.346** (1.100) (0.9870) Macro - Variables 0.2565* 0.2460* (0.1467) (0.1417) 0.0134 (0.1472) 0.1337 (0.2038) -0.0717*** -0.0622** (0.0266) (0.0245) 0.0807 (0.0533) 0.0388** (0.0197) Money supply (M2) (4) L.GDP/Capita INFGDP No of obs No of groups No of instrument AR1-test AR2-test Sargan-test Hansen p-value 0.0283 (0.1424) 1261 162 41 -2.67 [0.008] 0.91 [0.362] 36.55 [0.019] 0.714 0.0501 (0.0382) 1261 162 41 -2.73 [0.006] 0.55 [0.580] 39.09 [0.079] 0.765 GDP per capita (6) -11.0404 (11.1411) 0.5525*** (0.0781) 0.5360** (0.2615) -0.2119** (0.1061) -0.0027 (0.0297) -0.0237 (0.0911) 1.1392 (0.7696) -2.0679** (0.8526) 0.2322* (0.1370) 0.0517 (0.1272) -0.0651*** (0.0233) 0.0588 (0.0466) 0.0773 (0.1197) 1261 162 41 -2.77 [0.006] 0.89 [0.373] 39.70 [0.070] 0.726 Page | 68 Notes: All models were estimated with a constant Robust t-statistics are in parentheses Significance level at which the null hypothesis is rejected: ***, 1%; **, 5%; and *, 10% The model was estimated one-step SGMM estimator with difference lag For each regression are presented the number of observations (No Obs.) AR1 and AR2 tests are the Arellano–Bond tests for first and second-order autocorrelation in firstdifferenced errors; The statistics and p-values (in square brackets) for the Sargan-test of over-identifying restrictions and Hansen-test for uncorrelation between the instruments and residuals are also reported for the AB estimations The appreciated real effective exchange rate means appreciated domestic currency APPENDIX Additional analyses and Robustness checks Variables Constant Lagged NPLs SVCR LEVER INEFF NOINTINC LOGSIZE ROA UNEMP RINT REER L.GDPG INFGDP No of obs No of groups No of instrument AR1-test AR2-test year ≥ 2007 (7) -2.4997 (9.9146) 0.6034*** (0.0699) 0.2249 (0.2354) -0.0982 (0.0999) -0.0774* (0.0448) -0.0339 (0.0971) 0.6483 (0.5420) -2.734** (1.0950) 0.4470** (0.2195) 0.0525 (0.1734) -0.0151 (0.0182) 0.0014 (0.0357) 0.0627 (0 1644) 917 159 48 -2.54 [0.011] 0.41 [0.685] IDN out (8) -7.2192 (11.8557) 0.5942*** (0.1107) Bank-specifics 0.6097** (0.2428) -0.2017** (0 0859) -0.1039** (0.0423) -0.1289 (0.1046) 1.4037* (0.8462) -3.1629** (1.2715) Macro - Variables 0.8106*** (0.3077) 0.1930* (0.1081) -0.0813*** (0.0301) 0.0161 (0.0506) 0.1174 (0.1213) 838 92 55 -2.14 [0.033] 0.92 [0.359] Two-step SGMM (9) -6.9052 (6.1437) 0.5606*** (0.0741) 0.5627*** (0.1130) 0.3833*** (0.1306) -0.1437*** (0.0553) -0.0109 (0.0176) -0.0027 (0.0542) 0.8674* (0.4470) -2.0851*** (0.5018) 0.8776* (0.4927) -0.3497* (0.2052) -0.0040 (0.0232) -00012 (0.0618) 0.1907 (1.0371) -3.0477*** (1.0557) 0.1833* (0.1091) 0.0195 (0.1066) -0.0579** (0.0238) 0.0513 (0.0598) 0.1307* (0.0712) 1261 162 41 -2.73 [0.006] 0.84 [0.402] 0.3361 (0.2433) 0.0659 (0.1126) -0.0684*** (0.0251) 0.0492 (0.0481) 0.0816 (0.0935) 1053 152 33 -2.54 [0.011] 0.08 [0.938] DGMM (10) - Page | 69 Sargan-test Hansen p-value 60.87 [0.004] 0.742 35.87 [0.736] 0.776 39.71 [0.070] 0.728 27.02 [0.170] 0.786 Notes: All models were estimated with a constant Robust t-statistics are in parentheses Significance level at which the null hypothesis is rejected: ***, 1%; **, 5%; and *, 10% The model was estimated one-step SGMM estimator with difference lag For each regression are presented the number of observations (No Obs.) AR1 and AR2 tests are the Arellano–Bond tests for first and second-order autocorrelation in first-differenced errors; The statistics and p-values (in square brackets) for the Sargan-test of over-identifying restrictions and Hansentest for are also reported for the AB estimations The appreciated real effective exchange rate means appreciated domestic currency Page | 70 ... CHI MINH CITY, DECEMBER 2016 DECLARATION I declare that the wholly and mainly contents and the work presented in this thesis (Macro Economic Determinants of Credit risk in the ASEAN Banking System) ...UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MACRO ECONOMIC DETERMINANTS OF CREDIT. .. and credit risk management policy with the information of which economic and bank specific determinants of the bank influence credit risk Therefore, with information such as increase in the inflation