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Probleme des Qualitätsvergleichs von Kreditausfallprognosen

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  • Walter Krämer
  • Michael Bücker

Abstract

The statistical quality of credit default forecasts can be measured and compared in different ways. This article surveys the various approaches that have been suggested in the literature and discusses their respective properties. For the particular case of credit scoring in the retail business, it is shown that some quality criteria are more useful than others. In particular, various measures that are popular in, e.g. meteorology, such as the Brier score have to be applied with caution. Copyright Springer 2011

Suggested Citation

  • Walter Krämer & Michael Bücker, 2011. "Probleme des Qualitätsvergleichs von Kreditausfallprognosen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 5(1), pages 39-58, March.
    • Handle: RePEc:spr:astaws:v:5:y:2011:i:1:p:39-58
    DOI: 10.1007/s11943-011-0096-0
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    References listed on IDEAS

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    1. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    2. R. Winkler & Javier Muñoz & José Cervera & José Bernardo & Gail Blattenberger & Joseph Kadane & Dennis Lindley & Allan Murphy & Robert Oliver & David Ríos-Insua, 1996. "Scoring rules and the evaluation of probabilities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 5(1), pages 1-60, June.
    3. Lukas Menkhoff & Maik Schmeling & Ulrich Schmidt, 2010. "Are All Professional Investors Sophisticated?," German Economic Review, Verein für Socialpolitik, vol. 11(4), pages 418-440, November.
    4. Stein, Roger M., 2005. "The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1213-1236, May.
    5. Walter Krämer, 2006. "Evaluating probability forecasts in terms of refinement and strictly proper scoring rules," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 223-226.
    6. Lukas Menkhoff & Maik Schmeling & Ulrich Schmidt, 2010. "Are All Professional Investors Sophisticated?," German Economic Review, Verein für Socialpolitik, vol. 11(4), pages 418-440, November.
    7. D J Hand, 2005. "Good practice in retail credit scorecard assessment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1109-1117, September.
    8. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Kreditausfälle; Wahrscheinlichkeitsprognosen; Scorekarten; C53; G24; Credit default; Probability forecasts; Scorecards;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

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