Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/70472
Authors: 
Kiefer, Nicholas M.
Year of Publication: 
2007
Series/Report no.: 
CAE Working Paper 07-11
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 e-mixture of priors.
Subjects: 
Bayesian inference
robustness
expert information
Basel II
risk management
prior assessment
Document Type: 
Working Paper

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