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Estimation of rating class transition probabilities with incomplete data

机译:带有不完整数据的评估等级转换概率的估计

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This paper shows that the well known "duration" and "cohort" methods for estimating transition probabilities of external bond ratings are not suitable for internal rating data. More precisely, the duration method cannot and the cohort method should not be used in connection with banks' ratings generated from internal models. Structural differences within the borrower monitoring process of banks and rating agencies are responsible for this result. A Maximum Likelihood (ML) estimation procedure, which accounts for the peculiarities of internal bank ratings, is introduced and applied to data from a German bank. The empirical results indicate that the differences between cohort and ML transition matrices are both, statistically and economically significant. Furthermore, evidence of rating reversals, business cycle dependent transition probabilities and on the factors which determine the borrower monitoring intensity of banks is provided.
机译:本文表明,用于估计外部债券评级的过渡概率的众所周知的“持续时间”和“同类”方法不适用于内部评级数据。更确切地说,不能使用持续时间方法,也不应将同类方法与内部模型生成的银行评级结合使用。银行和评级机构的借款人监控过程中的结构差异是造成这一结果的原因。引入了最大似然(ML)估计程序,该程序考虑了内部银行评级的特殊性,并将其应用于来自德国银行的数据。实证结果表明,同类群组和ML转换矩阵之间的差异在统计和经济上均很重要。此外,还提供了评级逆转,与业务周期有关的过渡概率以及确定借款人对银行监控强度的因素的证据。

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