  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.
