Abstract

We consider a hidden Markov model of credit quality. We assume that the credit rating evolution can be described by a Markov chain but that we do not observe this Markov chain directly. Rather, it is hidden in “noisy” observations represented by the posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise. We derive smoothed estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model.

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