MSc student: Tatarici Roxana Lumini ţ a

1. The Academy of Economic Studies The Faculty of Finance, Insurance, Banking and Stock Exchange Doctoral School of Finance and Banking The impact of macroeconomic factors on the quality of household loans: an empirical approach for some CEE countries MSc student: Tatarici Roxana Luminiţa Supervisor: PhD Professor Moisă Altăr July 2010, Bucharest

2. Contents: • Motivation and objectives • Literature Review • Data • Results • Conclusions • Further research • References

3. Motivations and objectives • “Macroeconomic based models are motivated by the increase of default rates during recessions” (Chan-Lau, IMF (2006)). • This has been proven in the recent period when the ability of debtors to cover their debt has been shaken leading to an increase of non performing loans. • Increase of losses for banks and potential risks for financial stability • High importance of credit risk for the banking sector. • The necessity of financial authorities to identify and monitor the risks and imbalances for the entire banking system (macroprudentiality). Objectives: • To identify the macroeconomic variables that influence debtors capacity of repayment in order to efficiently asses the strengths and vulnerabilities of household loan portfolios; • To identify the period of transmission from the explanatory variables into the quality of household loans; • To define a multivariate framework which allows the development of top down stress testing exercises.

4. Brief literature review • Rinaldi and Arellano (2006) using a panel cointegration find an important impact of the debt burden on the borrowers capacity of repayment. For the short run relationship relevant factors for explaining NPL ratio are the unemployment rate, nominal interest rate, household wealth indices, house price index and the ratio of owner occuppied dwellings to stock of dwellings (to account for collateralized loans). • Pesola (2007) introduces non-linearities by means of a multiplying impact of financial fragility (loans to private sector/GDP). He proposes as explanatory factors a income surprise variable and interest rate surprise and tests for the joint impact of financial fragility and the mentioned factors. • Kalirai(2002), Boss(2002) and Boss et al. (2009) propose a classification of variables into six categories, which are further on tested in an univariate and multivariate framework. • Jakubik and Schmieder (2008) find that different factors affect the household portfolio in Czech Republic and respectively Germany. While for the first unemployment rate and real interest rate are the most important macroeconomic drivers, for Germany credit to GDP ratio and household income showed the strongest impact. • Marcucci and Quagliariello (2008) analyze the asymmetries in the relationship between credit risk and business cycles and conclude that: (i) riskier banks’ portfolios are more cyclical (more sensitive to business cycles) than less risky ones and (ii) cyclicality is more pronounced in bad/severe economic conditions.

5. Data: Quantifying the quality of portfolio by using NPL ratio Non performing loan ratio is one of the core financial soundness indicators proposed by the IMF and considered as an appropriate measure for the quality of loan portfolios. Consensus on the construction of the dependant variable (quarterly data 2005Q1: 2009Q4) Romania: the ratio of loans with more than 90 days past due /outstanding amount of household loans; Hungary: the ratio of loans with more than 90 days past due /outstanding amount of household loans; Poland: (substandard + doubtfull+loss)/ outstanding amount of household loans; Czech Republic: (substandard + doubtfull+loss)/ outstanding amount of household loans; Estonia : the ratio of loans with more than 60 days past due/ outstanding amount of household loans; Slovakia: bad loans/ outstanding amount of household loans. Advantages of using this measure for credit risk: - Facilitates the monitoring of credit risk at aggregate or individual levels; - Allows the development of macro-based models; - Starting to gain comparability: used as a macroprudential indicator by national regulators (Financial Soundness Indicators, IMF). Shortages in using this type of indicator: • Few data available and lack of information on types of loans; • Legislative changes may influence the volumes of non-performing loans within countries; • Backward indicator of credit risk;

6. Data: Explanatory variables proposed

7. Data: Similarities and differences among countries • Household indebtedness has grown considerably for all countries; • Similar path of NPL ratio (except for Czech Republic and Poland); • Severe contraction in economic growth, except for Poland and deterioration of consumer confidence; • Heterogeneity of portfolio structure: high proportion of consumer loans for Romania, while for Estonia the majority is represented by mortgage loans.

8. Results: Exploring for the individual impact of explanatory variables Fixed effects panel model accounts for country specific factors: financial education of debtors, socio-economic factors, legislation etc; ∆NPLt = f(∆xit), where xit: Significance levels: * -10%, **-5%, ***-1%.

9. Multi factor credit risk model (1) NPL ratio = f( GDP, unemployment rate, interest rate, exchange rate) Accounting for legislative changes: dumsk_06 (Slovakia 2006). Similar changes in Poland 2005 but uncertainty surrounding the robustness of the control variable.

10. Multi factor credit risk model (2) Lower probability of growing NPL ratio in Czech Republic and Poland (other factors affecting NPL ratio: restructuring process, well developed non performing market). Increased probability of growing non performing loans for Romania (large proportion of consumer loans) and Hungary (increasing proportion of consumer loans).

11. Multi-factor credit risk model (3) NPL ratio is considered as the PD for the household sector. Hypothesis: In a better state of the economy the rate of default is lower (Credit Portfolio View) Based on the specification of Virolainen (2004): modeling default rate for country i by a logistic functional form: Where: country specific macroeconomic index:

12. Scenario Analysis (1) • The baseline scenario relies on official projections for changes in real GDP and for the unemployment rate at the end of 2010. -Around 1% average annual growth for Romania, Hungary, Czech Republic and Estonia, while for Poland and Slovakia the perspectives for economic growth are more optimistic. - Small increase of unemployment rate. The difficulties could be however amplified by the risks associated by the problems that may appear on the reintegration in the labor market. • Average values from Bloomberg survey were considered for the third and last quarter of 2010 and for the second quarter the average daily values. • Because the transmission lag for money market rate was identified as three quarters we haven’t considered any change in interest rates, although in the case of an economic recovery the banks’ risk aversion might decrease and the deterioration of the quality of the portfolio might imply a reconsideration of the contractual agreements with the existent clients with problems to repayment (through restructuring their loans); • The pessimistic scenario is mainly based on a shock from the labor market: - An increase ofthe unemployment rate by the same amount as in 2009; - The worst case results of Bloomberg survey regarding the evolution of exchange rate; - Lower economic growth; • The optimistic scenario - A better evolution for the economic growth in 2010 (a symmetric increase of growth rates with 1 pp). - The changes in unemployment rate were considered at half the movements in moderate scenario. - The best case from Bloomberg Survey.

13. Scenario Analysis (2) • -Highest deterioration for the pessimistic scenario for Estonia; • -Slovakia would suffer also from an increase of approximate 8% at the end of the year in face of a slower • economic growth (around 2,3% on an annual basis) and an unemployment rate of almost 18% at 2010Q4. • - Similar increases for Czech Republic and Poland. • Increase of NPL ratio for Romania, Hungary and Slovakia, but with slower dynamics;

14. Conclusions • Shocks are rather rapidly transmitted in case of cyclical indicators (one or two lags), but it takes longer for the cost factors to affect the quality of loans (three quarters). • For the linear specification: - the largest impact from the unemployment rate. - the aggregate indebtedness ratio is not significant and does not exhibit the appropriate sign; - the structure of the portfolio does not explain the behavior of NPL ratio; • Consumer and mortgage loans are sensitive to the same factors, but the impact of macroeconomic shocks is different; They may also account for different explanatory variables such as real estate prices in case of mortgage loans. • Estonia and Slovakia are the most affected in case of the pessimistic scenario; • No single best fit model is desirable. Consensus among different specifications and if possible inclusion of individual specific factors.

15. Further research • Introduction of non-linearities conditional on debt burden, Pesola (2007); • As it has been shown the impact of some of the proposed variables is different for each type of loan, but the transmission period remains similar. Further improvement of the framework for sub-categories which can account for factor specific: LTV, real estate prices etc; • Incorporation of feedback effects from the financial sector into the real economy, Foglia (2008); • Indentification of asymmetries in debtors’ behavior is of great interest for both financial authorities, but also for credit institutions to ensure the efficiency of credit risk models. A reasonable length of time series is required; • Analysis at bank level data to account for differences in lending policies strategies (dynamics of credit growth), specificity of banks’ portfolios etc .

16. References (1) • Baboucek, I. and M. Jancar (2005) , “A VAR Analysis of the Effects of Macroeconomic Shocks to the Quality of the Aggregate Loan Portfolio of the Czech Banking Sector” , Czech National Bank, Working Paper Series 1/2005. • Boss.M (2002), “A macroeconomic credit risk model for stress testing the Austrian credit portfolio”, Financial Stability Report No.4. • Boss, M., G. Fenz, J.Pann, C. Puhr, M.Schneider and E.Ubl (2009), “Modeling Crdit risk through the Austrian business cycle: An update of the OeNB model”, Article published in the Financial Stability Report No.17 • Chan-Lau, J.(2006), “Fundamentals–Based estimation of default probabilities: A survey”, IMF Working Paper, WP/06/149. • Foglia,A (2008).”Stress testing credit risk: a survey of authorities’ approaches”, Bank of Italy Occasional Papers, No.37. • Gasha, J.G and R. A. Morales (2004), “Identifying threshold effects in credit risk stress testing”, IMF Working Paper WP/04/150. • Girault,G. and M.Alfredo (2008), “Modeling extreme but plausible losses for credit risk:a stress testing framework for the Argentine Financial System” MPRA paper No.16378. • Glogowski,A.(2008), “Macroeconomic determinants of Polish banks’ loan losses –results of a panle data study”, National Bank of Poland Working Paper, No. 53. • Jakubik, P. (2007), “Macroeconomic Environment and Credit Risk” ,Czech National Bank and the Institute of Economic Studies of Charles University. • Jakubik,P. and C.Schmieder, (2008),”Stress Testing Credit Risk:Comparison of the Czech Republic and Germany”, Financial Stability Institute. • Jimenez,G. and J.Saurina ,(2005), “Credit Cycles, Credit risk and prudential regulation”, Bank of Spain, Working Papers, No. 0531.

17. References (2) • Katai,R. (2010), “Credit risk model for the Estonian Banking Sector”, Bank of Estonia Working Papers, 1/2010. • Kalirai, H and M. Scheicher (2002), “Macroeconomic stress testing: Preliminary evidence for Austria”, Financial Stability Report No.3. • Kern, M. and B.Rudolph (2001), ‘Comparative Analysis of Alternative Credit Risk Models –an Application on German Middle Market Loan Portfolios-“, Center for Financial Studies WP No.2001/03. • Marcucci, J. and M. Quagliariello (2008), “Credit risk and business cycle over different regimes”, Bank of Italy Working Papers, No. 670. • Pesola, J. (2005a), “Banking fragility and distress: An econometric study of macroeconomic determinants”, Bank of Finland Research, Discussion Papers 13.2005. • (2007b) “Financial fragility, macroeconomic shocks and banks’ loan losses: evidence from Europe” , Bank of Finland Research, Discussion Papers 15.2007. • Rinaldi,L. and A.Sanchis-Arellano (2006), “Household debt sustainability. What explains household non-performing loans? An empirical analysis”, ECB Working Paper Series, No. 570. • Van den End,J.W, M.Hoeberichts and M.Tabbae (2006), “Modelling Scenario Analysis and Macro Stress-Testing”, DNB Working Paper, No.119. • Virolainen, K (2004), “Macro stress testing with a Macroeconomic Credit Risk Model for Finland”, Bank of Finland, Discussion Papers, No.18. • (2008)” Macro-model-based stress testing of Basel II capital requirements”, Bank of Finland, Discussion Papers, No.17.2008. • Zeman,J. and P.Jurca, (2008), “Macro stress testing of the Slovak banking sector”,Bank of Slovenia Working Paper 1/2008. • Wooldridge, J. (2001), “Econometric Analysis of cross section and panel data”.