Research On Default Probability Based On The Estimation Of Bayesian Approach

The Basel II Accord requires banks to establish rigorous statistical procedures for the estimation and validation of default and ratings transition probabilities. This raises great technical challenges when sufficient default data are not available, as is the case for low default portfolios. We develop a new model that describes the typical internal credit rating process used by banks. The model captures patterns of obligor heterogeneity and ratings migration dependence through unobserved systematic macroeconomic shocks. We describe a Bayesian hierarchical framework for model calibration from historical rating transition data, and show how the predictive performance of the model can be assessed, even with sparse event data. Finally, we analyze a rating transition data set from Standard and Poor's during 1993–2009.This paper addresses these issues and makes three contributions. Our first contribution consists in developing a new model that describes the typical credit rating process that most major banks employ. In general, an obligor is assigned a credit rating based on an assessment of its current credit worthiness, which depends on many systematic and firm specific variables. The model includes the effects of a shared unobserved macroeconomic shock which induces dependence among transition probabilities for different credit classes in any given period. The model takes into account the heterogeneity in the credit worthiness of obligors in the same credit class. Our second contribution consists in addressing the difficult issue of assessing the predictive performance of a model when event data are sparse. We employ two approaches to examine the predictive ability of a model. The first approach is based on a Bayesian measure of predictive power, the Deviance Information Criterion. When comparing the performance of several models, the model with the smallest DIC value is estimated to give the best predictions for a data set of the same structure as the data actually observed. Our second approach to investigating the predictive performance of a model is out-of-sample test. Credit rating process model is sparse in our research, and it is very important to the default probability of banks. Credit rating process model can be used in our financial system.