A Coupled Markov Chain Approach to Credit Risk Modeling
AbstractWe 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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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 0911.3802.
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
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Web page: http://arxiv.org/
Other versions of this item:
- Wozabal, David & Hochreiter, Ronald, 2012. "A coupled Markov chain approach to credit risk modeling," Journal of Economic Dynamics and Control, Elsevier, vol. 36(3), pages 403-415.
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G01 - Financial Economics - - General - - - Financial Crises
- C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
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