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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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    References listed on IDEAS

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    1. Kiefer, Nicholas M. & Larson, C. Erik, 2007. "A simulation estimator for testing the time homogeneity of credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 818-835, December.
    2. Garthwaite, Paul H. & Kadane, Joseph B. & O'Hagan, Anthony, 2005. "Statistical Methods for Eliciting Probability Distributions," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 680-701, June.
    3. Umesh Gavasakar, 1988. "A Comparison of Two Elicitation Methods for a Prior Distribution for a Binomial Parameter," Management Science, INFORMS, vol. 34(6), pages 784-790, June.
    4. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
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