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Bayesian Methods for Improving Credit Scoring Models

Author

Listed:
  • Posch Peter N.

    (University of Ulm)

  • Loeffler Gunter

    (University of Ulm)

  • Schoene Christiane

    (University of Ulm)

Abstract

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.

Suggested Citation

  • Posch Peter N. & Loeffler Gunter & Schoene Christiane, 2005. "Bayesian Methods for Improving Credit Scoring Models," Finance 0505024, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpfi:0505024
    Note: Type of Document - pdf; pages: 27
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/fin/papers/0505/0505024.pdf
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    References listed on IDEAS

    as
    1. Zellner, Arnold & Rossi, Peter E., 1984. "Bayesian analysis of dichotomous quantal response models," Journal of Econometrics, Elsevier, vol. 25(3), pages 365-393, July.
    2. 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.
    3. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    4. 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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    Cited by:

    1. Mestiri, Sami & Farhat, Abdejelil, 2018. "Credit Risk Prediction based on Bayesian estimation of logistic regression model with random effects," MPRA Paper 119960, University Library of Munich, Germany.

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    More about this item

    Keywords

    Credit Scoring; Bayesian Inference; Bankruptcy Prediction;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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