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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.

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Paper provided by Cornell University, Center for Analytic Economics in its series Working Papers with number 07-11.

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Date of creation: Jul 2007
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Handle: RePEc:ecl:corcae:07-11

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  1. Umesh Gavasakar, 1988. "A Comparison of Two Elicitation Methods for a Prior Distribution for a Binomial Parameter," Management Science, INFORMS, INFORMS, vol. 34(6), pages 784-790, June.
  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, American Statistical Association, vol. 100, pages 680-701, June.
  3. Pamela Nickell & William Perraudin & Simone Varotto, 2001. "Stability of ratings transitions," Bank of England working papers 133, Bank of England.
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