Default estimation and expert information: All likely dataset analysis and robust validation

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CAE Working Paper ; No. 07-11

Classification and Topics

• Classification: Wirtschaft
• Subject: Bayesian inference ; robustness ; expert information ; Basel II ; risk management ; prior assessment

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kiefer, Nicholas M.
• Event: Veröffentlichung
• (who): Cornell University, Center for Analytical Economics (CAE)
• (where): Ithaca, NY
• (when): 2007

Further information

• Handle: http://hdl.handle.net/10419/70472
• Last update: 10.03.2025, 11:44 AM CET