1. 1 Loan Loss Provisions Policy - Emerging vs. Developed Economies MSc student: Irina Gabriela Bidivenciu Supervisor: Professor Moisa Altar, PhD Bucharest, July 2007 The Academy of Economic Studies Doctoral School of Finance and Banking

2. 2 CONTENTS 1. Introduction 2. Literature review 3. The model 4. Estimation Results  Estimations of the Loan Loss Provisions Model using GLS  Estimations of the Loan Loss Provisions Model using GMM  Estimations of the Loan Loss Provisions Model within a commercial bank 5. Conclusions

3. 3 1.Introduction The modern economies are different from those in the past in their ability to identify the risk, to measure it, to appreciate its consequences and in taking action accordingly. The loan loss provisions are a “device” that can correct the negative effects of the loan portfolio problems within the bank sector. The level of the loan loss provisions must be designed to cover the expected losses during the economic cycle. Risk and/or Capital vs. Return

4. 4 2.Literature review The Basel Committee (1988)  new method for evaluating the capital - assets correlation based on a simplified weights system algorithm and a minimum capital adequacy ratio of 8%. Basel II (2004)  International Convergence of Capital Measurement and Capital Standards: all the credit institutions are required to have a policy in relation to credit risk, arrears and provisioning management. Pérez, D., Salas-Fumás, V., Saurina, J., (2006)  the banks must protect their capital from expected or unexpected losses through loan loss provisions and not to wait until the negative events occurred without affecting the transparency using the statistical provisions. Laeven, L. and Majnoni, G., (2002)  banks on average postpone provisioning when faced with cyclical upturns and favorable income conditions until negative conditions set in (income smoothing practices). Cavallo M. and Majnoni G., (2001)  the fiscal authority may affect relevant business decisions for banks and financial institutions. Fernández de Lis, S., Pagés, J. M. and Saurina J., (2000)  introduction of statistical provisions in Spain. In good times the banks have to set aside provisions that might be depleted in bad times when the excesses of the last upturn appear in the form of impaired assets.

5. 5 3.The Model 3.1. The Model Variables  Total Assets (A)  Loan Loss Provisions (LLP)  Profits Before Tax and Provisions (EBP)  Loan Growth in real terms (∆L)  Real Growth in GDP per capita (∆GDP) or Real Growth of Industrial Output Index (∆IOI) Note: The values of the loan loss provisions at time t correspond to the values of the assets at time t-1.  Data Source: Bankscope EUROSTAT BNR

6. 6 3.2. The Model Hypothesis of Prudent Loan Loss Provisioning Behavior. Data filters The coefficient on earnings before tax and provisions is negative; The coefficient on loan growth is negative; The coefficient on real growth rate of GDP per capita / the real growth of the Industrial Output Index is negative. The bank/year observations that exhibit one of the following features were excluded: o Ratio of loan loss provisions over lagged total assets > 90% or <10 %; o Earnings before provisions over lagged total assets > 12% o Loan growth rate in real terms > 56% o Loan decreasing rate in real terms > 50%

7. 7 3.3. The Model Description Testing the hypothesis of imprudent behavior and verifying the nature of the relationship between banks’ provisions and earnings: (1) The speed of adjustment of the dependent lagged variable is depicted through: (2)

8. 8 3.4. Correlation matrix Developed Economies  Income smoothing  Imprudent behavior  Anti-business cyclical behavior Emerging Economies  No Income smoothing  Imprudent behavior  No anti-business cyclical behavior

9. 9 4.The Estimations Results 4.1. Generalized Least Squares Developed EconomiesEmerging Economies

10. 10 4.1. Generalized Least Squares (contd.) Running the GLS estimates  the results are different amongst the developed and emerging economies  The banks within developed countries smooth the income while within the emerging countries this is not a common practice;  The loan loss provisions follow the loan portfolio growth only within the emerging economies;  The loan loss provisions policies are correlated with the economic cycle.

11. 11 4.1. Generalized Least Squares (contd.) Testing the stationarity  Levin, Lin & Chu Testing the robustness of the estimations  Hausman Test (endogeneity test)  Developed Countries: the fixed effects results do not differ significantly from the random effects results  Emerging Countries: when running an auxiliary regression the resid term takes value of 0.001

12. 12 4.1. Generalized Least Squares – negative earnings dummy (contd.) Emerging Economies  Hausman test - robustness GLS – negative earnings dummyGLS – negative earnings dummy + resid

13. 13 4.2. Generalized Method of Moments Developed EconomiesEmerging Economies

14. 14 4.2. Generalized Method of Moments (contd.) Running the GMM estimates  the results are different amongst the developed and emerging economies  All the banks considered are slow in adjusting their provisions over a certain number of years as suggest the slow decrease of the lagged dependent variable coefficient.  The banks within the developed countries smooth their earnings while within the emerging countries this is not a common practice.  The banks within the developed countries have an imprudent behaviour regarding provisioning while the others are showed to be prudent in their polices;  The loan loss provisions polices follow the economic cycle only within the banks from Western Europe.

15. 15 4.2. Generalized Method of Moments – negative earnings dummy (contd.) Emerging Economies  Hausman test - robustness GMM – negative earnings dummyGMM – negative earnings dummy + resid

16. 16 4.3. A Commercial Bank / monthly data Similar model for a commercial Romanian bank  the bank behavior between 1st of June 2004 and 31st of March 2007 Test the stationarity  Augmented Dickey Fuller The estimates results: 1. Prudent behavior of the bank management regarding provisioning; 2. The relation with the economic cycle: Industrial Output Index  the overall portfolio exposure with the industrial sector represents about 25 percent of the total loan portfolio exposure; 3. No income smoothing

17. 17 4.3. A Commercial Bank / monthly data (contd.) OLS estimatesGMM estimates

18. 18

19. 19 Developed Economies – Loans

20. 20 Developed Economies – Loan Loss Provisions

21. 21 Emerging Economies – Loans

22. 22 Emerging Economies – Loan Loss Provisions

23. 23 5.Conclusions  The banks within the developed countries provision less during high GDP growth, suggesting an undesirable anti-business cyclical behavior. On the contrary, the banks behavior from the emerging countries does not follow the economical cycle. The reason of this behavior is related with the economy development and the boom of the bank sector within all those countries;  The banks from developed countries smooth their income through the loan loss provisioning policies. This might result in lower earnings quality since net income does not representatively portray the economic performance of the business entity for the period. The banks from the emerging countries do not smooth their income;

24. 24 5.Conclusions (contd.)  The amounts allocated to the loan loss provisions in the emerging countries follow the growth of the loan portfolio showing a prudent behavior of the banks’ managers accordingly with the new fiscal and prudential requirements;  Credit risk is a normal part of banking. However, where the amount of risk is excessive or where this is not properly monitored and controlled, it can produce a significant threat to the credit institution and its earnings.

25. 25 References Basel Committee on Banking Supervision (2006), “Sound credit risk assessment and valuation for loans”, Bank for International Settlements Cavallo, M and Majnoni, G (2001), “Do Banks Provision for Bad Loans in Good Times, Empirical Evidence and Policy Implications”, World Bank Research Working Paper No. 2619 Crouhy, M., Galai, D. and Mark R. (2006), “The Essentials of Risk Management”, The McGraw- Hill Companies, Inc, New York Cossin, D. and Pirotte H. (2001), “Advanced Credit Risk Analyses – Financial Approaches and Mathematical Models to Assets, Price and Manage Credit Risk”, John Wiley & Sons, Inc, New York Epstein, B. J. and Mirza A.A. (2002), “IAS 2002 Interpretation and Application of International Accounting Standards”, John Wiley & Sons, Inc, New York Fernández de Lis, S. F., Pagés, J. M., Saurina, J., (2000), “Credit growth, problem loans and the credit risk provisioning in Spain”, Banco de España – Servicio de Estudios, Documento de Trabajo No. 0018 Fisher, S., (2003), “Implications of the Basel II for Emerging Market Countries”, The William Taylor Memorial Lectures No. 7, Group of trinity, Washington, DC Hansen, B.E. and West, K.D. (2002), “Generalized Method of Moments and Macroeconomics”, Journal of Business & Economic Statistics Laeven, L and Majnoni, G (2002), “Loan Loss Provisioning and the Economic Slowdowns: Too Much, Too Late?”, Conference Series, Federal Reserve Bank of Boston Levine, A and Lin, C-F (1992), “Unit Root Tests in Panel Data: Asymptotic and Finite-sample Properties”, University of California, San Diego, Department of Economics, Discussion Paper 92- 23

26. 26 References (contd.) Mazararu, E, (2005), “The New Basel Accord”, The Corporate Development Sector, Raiffeisen Bank Pérez, D., Salas-Fumás V., Saurina, J., (2006), “Earnings and capital management in alternative loan loss provision regulatory regimes”, Banco de España, Documento de Trabajo No. 0614 Pynnonen, S. (2007), “A Short Introduction to the Generalized Method of Moments Estimation”, University of Vaasa, Department of Mathematics and Statistics, Finland Keller G. and Warrack B. (2001), “Statistics for Management and Economics”, Fifth Edition, Duxbury Thomson Learning Yaffee, R., (2003), “A primer for Panel Data Analysis”, Connect Information Technology at New York University, Information Technology Services Wooldridge, J. M., (2001), “Econometric Analysis of Cross Section and Panel Data”, The MIT Press, Cambridge, Massachusetts, London, New England **** Credit Policy (2007), Raiffeisen Bank **** Annual Reports (2004, 2005, 2006), Raiffeisen Bank EUROSTAT, General and regional statistics, Economy and finance indicators BANKSCOPE, Bureau Van Duk National Bank of Romania, Annual Reports and Monthly Bulletins National Bank of Romania, Regulation No. 5 (2002)