Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data
AbstractCredit to the private sector has risen rapidly in European emerging markets but its risk evaluation has been largely neglected. Using retail-loan banking data from the Czech Republic we construct two credit risk models based on logistic regression and Classification and Regression Trees. Both methods are comparably efficient and detect similar financial and socio-economic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources) that performs very well. This way we confirm significance of socio-demographic variables and link our results with specific issues characteristic to new EU members.
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Bibliographic InfoPaper provided by William Davidson Institute at the University of Michigan in its series William Davidson Institute Working Papers Series with number wp1015.
Date of creation: 01 Apr 2011
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credit scoring; discrimination analysis; banking sector; pattern recognition; retail loans; CART; European Union;
Other versions of this item:
- Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 47(6), pages 80-98, November.
- B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- P43 - Economic Systems - - Other Economic Systems - - - Finance; Public Finance
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- JosÃƒÂ© Luis Gallizo & Ramon Saladrigues & Manuel Salvador, 2010. "Financial Convergence in Transition Economies: EU Enlargement," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 46(3), pages 95-114, May.
- Gabrisch, Hurbert & Orlowski, Lucjan, 2009. "Interest Rate Convergence in the Euro-Candidate Countries: Volatility Dynamics of Sovereign Bond Yields," Working Papers 2009001, Sacred Heart University, John F. Welch College of Business.
- Evžen Koèenda & Jan Hanousek & Peter Ondko, 2007. "The Banking Sector in New EU Member Countries: A Sectoral Financial Flows Analysis (in English)," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 57(5-6), pages 200-224, August.
- Alexis Derviz & Jiri Podpiera, 2004.
"Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic,"
2004/01, Czech National Bank, Research Department.
- Alexis Derviz & JiÅÃ Podpiera, 2008. "Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 44(1), pages 117-130, January.
- Ranjula Bali Swain, 2007. "The demand and supply of credit for households," Applied Economics, Taylor & Francis Journals, vol. 39(21), pages 2681-2692.
- Ceyla Pazarbasioglu & Gudrun Johnsen & Paul Louis Ceriel Hilbers & Inci Ã–tker, 2005. "Assessing and Managing Rapid Credit Growth and the Role of Supervisory and Prudential Policies," IMF Working Papers 05/151, International Monetary Fund.
- David A Grigorian & Vlad Manole, 2006.
"Determinants of Commercial Bank Performance in Transition: An Application of Data Envelopment Analysis,"
Comparative Economic Studies,
Palgrave Macmillan, vol. 48(3), pages 497-522, September.
- David A. Grigorian & Vlad Manole, 2002. "Determinants of Commercial Bank Performance in Transition: An Application of Data Envelopment Analysis," IMF Working Papers 02/146, International Monetary Fund.
- Grigorian, David A. & Manole, Vlad, 2002. "Determinants of commercial bank performance in transition - an application of data envelopment analysis," Policy Research Working Paper Series 2850, The World Bank.
- Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
- Martin Rezac & Frantisek Rezac, 2011. "How to Measure the Quality of Credit Scoring Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 486-507, November.
- Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research Department.
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