The real nature of credit rating transitions#

It is well known that credit rating transitions exhibit a serial correlation also known as a rating drift. This is clearly confirmed by this analysis, which also reveals that the credit rating migration process is additionally influenced by three non-observable hidden risk situations. This finding violates the common stationary assumption. The hidden risk situations in turn serially depend on each other in successive observation periods. Taken together, they represent the memory of a credit rating transition process and influence the future transient process. To take this into account, I introduce an extension of a higher order Markov model and a new Markov mixture model. These models allow me to capture these inherent serial correlation structures, to bypass the stationary assumption and to model the process as time non-homogeneous. An algorithm is introduced to derive a single transition matrix with the new additional information. Finally, by means of different CVaR simulations by CreditMetrics, I show that the standard Markov process overestimates the economic risk