A Coupled Markov Chain Approach to Credit Risk Modeling

We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating
transitions process. The parameters of the model are estimated by a maximum likelihood
approach using historical rating transitions and heuristic global optimization techniques.
We benchmark the model against a GLMM model in the context of bond portfolio risk
management. The proposed model yields stronger dependencies and higher risks than the
GLMM model. As a result, the risk optimal portfolios are more conservative than the
decisions resulting from the benchmark model.