Credit Rating Dynamics in the Presence of Unknown Structural Breaks

In many credit risk and pricing applications, credit transition matrix is modeled by a constant transition probability or generator matrix for Markov processes. Based on empirical evidence, we model rating transition processes as piecewise homogeneous Markov chains with unobserved structural breaks. The proposed model provides explicit formulas for the posterior distribution of the time-varying rating transition generator matrices, the probability of structural break at each period and prediction of transition matrices in the presence of possible structural breaks. Estimating the model by credit rating histories, we show that the structural break in rating transitions can be captured by the proposed model and structural breaks in rating transitions are dependent on industry sectors. We also find the extent of rating drift effect is heterogeneous given firms' previous ratings. We then compare the prediction performance of the proposed and time-homogeneous Markov chain models