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 mainly influenced by three completely different non-observable hidden risk situations, providing an individual environment for each successive rating. This finding violates the common stationary assumption. The hidden risk situations in turn also serially depend on each other in successive periods. Taken together, both represent the memory of a credit rating transition process and influence the future rating. To take this into account, I introduce an extension of a higher order Markov model and a new Markov mixture model. Especially the later one allows capturing these complex correlation structures, to bypass the stationary assumption and to take each hidden risk situation into account. 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.