Credit rating dynamics and Markov mixture models
详细信息 查看全文
- 作者：Halina Frydman ; Til Schuermann
- 关键词：Risk management ; Credit risk ; Credit derivatives
- 刊名：Journal of Banking and Finance
- 出版年：2008
- 出版时间：June 2008
- 年：2008
- 卷：32
- 期：6
- 页码：1062-1075
- 全文大小：255 K
文摘
Despite mounting evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. Based on empirical evidence, we propose a parsimonious model that is a mixture of (two) Markov chains, where the mixing is on the speed of movement among credit ratings. We estimate this model using credit rating histories and show that the mixture model statistically dominates the simple Markov model and that the differences between two models can be economically meaningful. The non-Markov property of our model implies that the future distribution of a firm’s ratings depends not only on its current rating but also on its past rating history. Indeed we find that two firms with identical current credit ratings can have substantially different transition probability vectors. We also find that conditioning on the state of the business cycle or industry group does not remove the heterogeneity with respect to the rate of movement. We go on to compare the performance of mixture and Markov chain using out-of-sample predictions.