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Default Estimation and Expert Information: All Likely Dataset Analysis and Robust Validation

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  • Kiefer, Nicholas M.

    (Cornell U)

Abstract

Default is a rare event, even in segments in the midrange of a bank's portfolio. Inference about default rates is essential for risk management and for compliance with the requirements of Basel II. Most commercial loans are in the middle-risk categories and are to unrated companies. Expert information is crucial in inference about defaults. A Bayesian approach is proposed and illustrated using a prior distribution assessed from an industry expert. The method of All Likely Datasets, based on sufficient statistics and expert information, is used to characterize likely datasets for analysis. A check of robustness is illustrated with an epsilon-- mixture of priors.

Suggested Citation

  • Kiefer, Nicholas M., 2007. "Default Estimation and Expert Information: All Likely Dataset Analysis and Robust Validation," Working Papers 07-11, Cornell University, Center for Analytic Economics.
    • Handle: RePEc:ecl:corcae:07-11
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    File URL: https://cae.economics.cornell.edu/07-11.pdf
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