We apply a Coupled Markov Chain approach to model rating transitions and thereby default probabilities of companies. We estimate parameters by applying a maximum likelihood estimation using a large set of historical ratings. Given the parameters the model can be used to simulate scenarios for joint rating changes of a set of companies, enabling the use of contemporary risk managementtechniques. We obtain scenarios for the payment streams generated by CDX contracts and portfolios of such contracts. This allows for assessing the risk of the current position held and design portfolios which are optimal relative to the risk preferences of the investor .
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 .
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apply a Coupled Markov Chain approach to model rating transitions and thereby default probabilities of companies. We estimate parameters by applying
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
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estimation using a large set of historical ratings. Given the parameters the model can be used to simulate scenarios for joint rating changes of a set of companies, enabling the use of contemporary risk managementtechniques. We obtain scenarios for the payment streams generated by CDX contracts and portfolios of such contracts. This allows for assessing the risk of the current position held and design portfolios which are optimal relative to the risk preferences of the investor
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