Credit rating process and estimation of transition probabilities: a Bayesian approach
Subject
Management Science and Operations
Publishing details
Decision Sciences Working Paper
Publication Year
2007
Abstract
This paper introduces a new methodology for modeling and estimating transition probabilities between credit classes. The paper makes three contributions. First, we develop a new statistical model that describes the typical credit rating process that most major banks employ. Second, we describe a Bayesian hierarchical framework for model calibration, using Markov Chain Monte Carlo techniques implemented through Gibbs sampling. This approach allows us to address the technical issues related to the estimation of default probabilities from low default portfolios. Third, we apply this methodology to the analysis of an extended rating transitions data set from Standard and Poor's between 1981--2004, and we examine both the in-sample and out-of-sample performance of the credit rating process model relative to that of the traditional latent factor approach. The results of this paper provide a framework that banks and other financial institutions can use to show that their internal rating systems produce estimates of rating transition probabilities that are "reasonable" from a regulatory perspective. This is particularly relevant in light of the New Basel Capital Accord that allows banks to use their own estimates of the probability of default for capital budgeting decisions, subject to regulatory approval.
Series
Decision Sciences Working Paper
Available on ECCH
No
