Macro economic determinants of credit risks in the asean banking system

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 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 CONTENTS DECLARATION i ACKNOWLEDGEMENT .ii ABBREVIATION iii CONTENTS v APPENDIX LIST OF TABLES CHAPTER 1: OVERVIEW OF RESEARCH .3 Introduction .3 1.1 Backgrounds: .3 1.2 Problem statements: 1.3 Research objectives: 1.4 Research questions: .6 1.5 Hypothesis of the study .6 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 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 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 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 CHAPTER 1: OVERVIEW OF RESEARCH Introduction: Banks are the financial intermediaries who play an important role in the development of a country In the financial sector, a commercial bank is a funding channel, which can allocate the cash flows in the economy through their financial services as well as traditional services (taking deposits and make business loans) Whenever a loan is approved, banks gain profits from the borrowers by loan interest rate and services fees However, banks would expose to credit risk from this service because borrowers could suddenly lost their abilities to pay the loan in time, namely the nonperforming loans (NPLs) The main reason for that comes from the movement of the macroeconomic environment, which directly impacts to the revenues and business activities of bank borrowers Therefore, this paper will conduct an examination about how the economics determinants affect the bank credit risk In this chapter, the backgrounds, problem statements, research objectives, research questions, significance of the research and the layouts will be discuss around this issue 1.1 Backgrounds: Along with the expansion of the economy as well as financial liberalization process in developing countries, the financial sector 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Roodman, 2009; Arellano & Bond, 1991 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 Money supply (M2) (4) -10.5152 (11.0380) 0.5715*** (0 0713) Interest rate spread (5) -11.3040 (11.7284) 0.5385*** (0.0823) Bank-specifics 0.5168* 0.5412** (0.2772) (0.2723) -0.2033* -0.2167* (0.1188) (0.1113) -0.0024 -0.0040 (0.0277) (0.0355) -0.0326 -0.0090 (0.0839) (0.1138) 1.1340 1.1503 (0.7988) (0.8109) -2.346** -2.0048** (0.9870) (1.100) Macro - Variables 0.2460* 0.2565* (0.1417) (0.1467) 0.0134 (0.1472) 0.1337 (0.2038) -0.0622** -0.0717*** (0.0266) (0.0245) 0.0807 (0.0533) 0.0388** (0.0197) No of obs No of groups No of instrument AR1-test AR2-test 0.0283 (0.1424) 1261 162 41 -2.67 [0.008] 0.91 [0.362] -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 L.GDP/Capita INFGDP GDP per capita (6) 0.0501 (0.0382) 1261 162 41 Sargantest 36.55 [0.019] (0.0466) 0.0773 (0.1197) 1261 162 41 -2.73 [0.006] 0.55 [0.580] Page | 69 39.09 [0.079] Hansen p-value 0.714 7 0.765 [0.006] 0.89 [0.373] 39.70 [0.070] 0.726 Page | 70 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 year ≥ 2007 (7) -2.4997 IDN out (8) -7.2192 Two-step SGMM (9) -6.9052 (9.9146) (11.8557) (6.1437) 0.5942*** (0.1107) 0.6034** Bank-specifics * 0.6097** (0.0699) (0.2428) -0.2017** 0.2249 (0 0859) (0.2354) -0.1039** -0.0982 (0.0423) (0.0999) -0.1289 -0.0774* (0.1046) (0.0448) 1.4037* -0.0339 (0.8462) (0.0971) -3.1629** 0.6483 (1.2715) (0.5420) Macro - Variables -2.734** 0.8106*** (1.0950) (0.3077) 0.1930* 0.4470** (0.1081) (0.2195) -0.0813*** 0.0525 (0.0301) (0.1734) 0.0161 -0.0151 (0.0506) (0.0182) 0.1174 0.0014 (0.1213) (0.0357) 0.0627 (0 1644) 917 838 159 92 48 55 DGMM (10) - 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) 0.3361 (0.2433) 0.0659 (0.1126) -0.0684*** (0.0251) 0.0492 (0.0481) 0.0816 (0.0935) 1261 162 41 1053 152 33 AR1-test AR2-test -2.54 [0.011] 0.41 [0.685] -2.14 [0.033] 0.92 [0.359] -2.73 [0.006] 0.84 [0.402] -2.54 [0.011] 0.08 [0.938] 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 firstdifferenced errors; The statistics and p-values (in square brackets) for the Sargan-test of over-identifying restrictions and Hansen- test for are also reported for the AB estimations The appreciated real effective exchange rate means appreciated domestic currency Page | 70 ... 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... 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