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The Bayesian Approach to Default Risk: A Guide

Author

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  • Jacobs, Michael

    (US Department of the Treasury)

  • Kiefer, Nicholas M.

    (Cornell University)

Abstract

A Bayesian approach to default rate estimation is proposed and illustrated using a prior distribution assessed from an experienced industry expert. The principle advantage of the Bayesian approach is the potential for coherent incorporation of expert information--crucial when data are scarce or unreliable. A secondary advantage is access to efficient computational methods such as Markov Chain Monte Carlo. After a preliminary discussion of elicitation of expert beliefs, all steps in a thorough Bayesian analysis of a default rate are illustrated. Using annual default rate data from Moody's (1999-2009) and a prior elicited from an industry expert, we estimate three structural credit models in the asymptotic single risk factor (ASRF) class underlying the Basel II framework (Generalized Linear and Generalized Linear Mixed Models), using a Markov Chain Monte Carlo technique.

Suggested Citation

  • Jacobs, Michael & Kiefer, Nicholas M., 2010. "The Bayesian Approach to Default Risk: A Guide," Working Papers 10-01, Cornell University, Center for Analytic Economics.
    • Handle: RePEc:ecl:corcae:10-01
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    References listed on IDEAS

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    1. McNeil, Alexander J. & Wendin, Jonathan P., 2007. "Bayesian inference for generalized linear mixed models of portfolio credit risk," Journal of Empirical Finance, Elsevier, vol. 14(2), pages 131-149, March.
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    5. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
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    Cited by:

    1. Carlos Perez Montes, 2015. "Estimation of Regulatory Credit Risk Models," Journal of Financial Services Research, Springer;Western Finance Association, vol. 48(2), pages 161-191, October.
    2. Yi-Ping Chang & Chih-Tun Yu, 2014. "Bayesian confidence intervals for probability of default and asset correlation of portfolio credit risk," Computational Statistics, Springer, vol. 29(1), pages 331-361, February.
    3. V L Miguéis & D F Benoit & D Van den Poel, 2013. "Enhanced decision support in credit scoring using Bayesian binary quantile regression," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(9), pages 1374-1383, September.
    4. Aneta Ptak-Chmielewska & Paweł Kopciuszewski, 2022. "New Definition of Default—Recalibration of Credit Risk Models Using Bayesian Approach," Risks, MDPI, vol. 10(1), pages 1-16, January.

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