The Credit Rating Process and Estimation of Transition Probabilities: A Bayesian Approach Catalina Stefanescu a,∗ , Radu Tunaru b , Stuart Turnbull c a Management Science and Operations, London Business School, London, UK b Cass Business School, London, UK c Bauer College of Business, University of Houston, US Abstract 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 sys- tematic macroeconomic shocks. We describe a Bayesian hierarchical framework for model calibration from historical rating transition data, and show how the predic- tive 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 1981– 2007. Our results have implications for the current Basel II policy debate on the magnitude of default probabilities assigned to low risk assets. JEL classification: G21; G28; G32; C11; C13; C52 Key words: Ratings transitions, Bayesian inference, Latent factors, Markov Chain Monte Carlo 1 Introduction The internal ratings based (IRB) approach in the New Basel Capital Ac- cord (Basel II) allows banks to use their own internal credit ratings. Banks need to estimate the entire matrix of transition probabilities between rating ∗ Corresponding author. Management Science and Operations, London Business School, Regent’s Park, London NW1 4SA, United Kingdom. Email address: cstefanescu@london.edu (Catalina Stefanescu). Preprint submitted to Elsevier 10 October 2008