A Dependent Hidden Markov Model of Credit Quality

Abstract EN

We propose a dependent hidden Markov model of credit quality.

We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings.

The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent.

The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm.

The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.