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.
|Date of creation:||Nov 2009|
|Date of revision:||Jan 2014|
|Publication status:||Published in Journal of Economic Dynamics and Control 36(3): 403-415. 2012|
|Contact details of provider:|| Web page: http://arxiv.org/|
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