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| Default Estimation and Expert Information by Nicholas M. Kiefer of Cornell University February 7, 2008 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 binomial model, most common in applications, is extended to allow correlated defaults. A check of robustness is illustrated with an є- mixture of priors. Keywords: Bayesian inference, robustness, correlated defaults, Basel II, risk management, prior assessment. Books Referenced in this Paper: (what is this?)
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