Center for Risk and Reliability, University of Maryland, College Park, Maryland, USA.
Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques
Article first published online: 16 NOV 2012
© 2012 Society for Risk Analysis
Volume 32, Issue 11, pages 1888–1900, November 2012
How to Cite
Kazemi, R. and Mosleh, A. (2012), Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques. Risk Analysis, 32: 1888–1900. doi: 10.1111/j.1539-6924.2012.01915.x
- Issue published online: 16 NOV 2012
- Article first published online: 16 NOV 2012
- Credit risk;
- Bayesian methods;
- default probability;
- model uncertainty
Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from “nominal predictions” due to “upsetting events” such as the 2008 global banking crisis.