The Real Nature of Credit Rating Transitions

Abstract

It is well known that credit rating transitions exh ibit a serial correlation, also known as rating drift, which is clearly confirmed by this analysis. Furthermore, it reveals that the credit rating migration process is mainly influenced by three com pletely different non-observable hidden risk situations with completely different transition pro babilities. This finding gains the deepest additional information on the violation of the comm only assumed 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 rat ing transition process and influence the future rating. To take this into account, I introduce an e xtension 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 sin gle transition matrix with the new additional information. Finally, by means of different CVaR si mulations by CreditMetrics, I show that the standard Markov process overestimates the economic risk.