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Definition of Default and Quality of Scoring Functions


  • Jiri Witzany



A retail bank consumer loan dataset is used to develop logistic regression based scoring functions with different definitions of default from a very broad to a narrow or hard. The performance of the scoring functions is compared with respect to the hard definition of default which indicates real losses suffered by the bank. The results confirm the hypothesis that the scoring functions developed on softer definitions of default perform worse than those developed on harder definitions of default. The conclusion is put into contrast with the observation that the Basel II regulation gives an incentive to use a rather soft definition of default in the rating and scoring process.

Suggested Citation

  • Jiri Witzany, 2011. "Definition of Default and Quality of Scoring Functions," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 18(28).
  • Handle: RePEc:czx:journl:v:18:y:2011:i:28:id:178

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    References listed on IDEAS

    1. Dirk Tasche, 2004. "The single risk factor approach to capital charges in case of correlated loss given default rates," Papers cond-mat/0402390,, revised Feb 2004.
    2. 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.
    3. Acharya, Viral V. & Bharath, Sreedhar T. & Srinivasan, Anand, 2007. "Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries," Journal of Financial Economics, Elsevier, vol. 85(3), pages 787-821, September.
    4. Jiri Witzany, 2011. "A Two Factor Model for PD and LGD Correlation," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 18(28).
    5. Jon Frye, 2000. "Depressing recoveries," Emerging Issues, Federal Reserve Bank of Chicago, issue Oct.
    6. Stefano Caselli & Stefano Gatti & Francesca Querci, 2008. "The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans," Journal of Financial Services Research, Springer;Western Finance Association, vol. 34(1), pages 1-34, August.
    7. Seidler, Jakub & Horvath, Roman & JakubĂ­k, Petr, 2009. "Estimating expected loss given default in an emerging market: the case of Czech Republic," Journal of Financial Transformation, Capco Institute, vol. 27, pages 103-107.
    8. De Graeve, F. & Kick, T. & Koetter, M., 2008. "Monetary policy and financial (in)stability: An integrated micro-macro approach," Journal of Financial Stability, Elsevier, vol. 4(3), pages 205-231, September.
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    Cited by:

    1. 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.

    More about this item


    credit risk; regulatory capital; default; scoring;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation


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