Bayesian Methods for Improving Credit Scoring Models
We propose a Bayesian methodology that enables banks to improve their credit scoring models by imposing prior information. As prior information, we use coefficients from credit scoring models estimated on other data sets. Through simulations, we explore the default prediction power of three Bayesian estimators in three different scenarios and find that they perform better than standard maximum likelihood estimates. We recommend that banks consider Bayesian estimation for internal and regulatory default prediction models.
References listed on IDEAS
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- Zellner, Arnold & Rossi, Peter E., 1984. "Bayesian analysis of dichotomous quantal response models," Journal of Econometrics, Elsevier, vol. 25(3), pages 365-393, July.
- Grunert, Jens & Norden, Lars & Weber, Martin, 2005.
"The role of non-financial factors in internal credit ratings,"
Journal of Banking & Finance,
Elsevier, vol. 29(2), pages 509-531, February.
- Grunert, Jens & Norden, Lars & Weber, Martin, 2002. "The Role of Non-financial Factors in Internal Credit Ratings," CEPR Discussion Papers 3415, C.E.P.R. Discussion Papers.
- Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January. Full references (including those not matched with items on IDEAS)
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