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We propose a Markov chain model for credit rating changes. We do not use anydistributional assumptions on the asset values of the rated companies butdirectly model the rating transitions process. The parameters of the model areestimated by a maximum likelihood approach using historical rating transitionsand heuristic global optimization techniques.  We benchmark the model against a GLMM model in the context of bond portfoliorisk management. The proposed model yields stronger dependencies and higherrisks than the GLMM model. As a result, the risk optimal portfolios are moreconservative than the decisions resulting from the benchmark model.

A Coupled Markov Chain Approach to Credit Risk Modeling