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首页> 外文期刊>Journal of banking & finance >Credit rating dynamics and Markov mixture models
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Credit rating dynamics and Markov mixture models

机译：信用评级动态和马尔可夫混合模型

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摘要

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.
机译：尽管有相反的证据，但通常假定在许多信用风险和定价应用程序中使用的信用迁移矩阵是由简单的马尔可夫过程生成的。基于经验证据，我们提出了一个简约模型，该模型是（两个）马尔可夫链的混合，其中混合取决于信用评级之间的移动速度。我们使用信用评级历史记录来评估此模型，并表明混合模型在统计上主导了简单的马尔可夫模型，并且两个模型之间的差异可能具有经济意义。我们模型的非马尔可夫性质意味着，公司评级的未来分布不仅取决于其当前评级，还取决于其过去的评级历史。实际上，我们发现具有相同当前信用评级的两家公司可以具有实质上不同的过渡概率向量。我们还发现，以商业周期或行业集团的状态为条件并不能消除变动率的异质性。我们继续使用样本外预测比较混合物和马尔可夫链的性能。

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