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.
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.:
- Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-24, January.
- 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.
- Zellner, Arnold & Rossi, Peter E., 1984. "Bayesian analysis of dichotomous quantal response models," Journal of Econometrics, Elsevier, vol. 25(3), pages 365-393, July.
When requesting a correction, please mention this item's handle: RePEc:wpa:wuwpfi:0505024. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA)
If references are entirely missing, you can add them using this form.