Default Estimation and Expert Information: All Likely Dataset Analysis and Robust Validation
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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- Pamela Nickell & William Perraudin & Simone Varotto, 2001.
"Stability of ratings transitions,"
Bank of England working papers
133, Bank of England.
- 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.
- 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.
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