英文标题：
《Capturing Model Risk and Rating Momentum in the Estimation of
  Probabilities of Default and Credit Rating Migrations》
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作者：
Marius Pfeuffer, Goncalo dos Reis, Greig smith
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最新提交年份：
2020
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英文摘要：
  We present two methodologies on the estimation of rating transition probabilities within Markov and non-Markov frameworks. We first estimate a continuous-time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computational time needed for the Wald confidence interval by a factor of a half. We provide an efficient procedure for transferring such uncertainties from the generator matrix of the Markov chain to the corresponding rating migration probabilities and, crucially, default probabilities.   For our second contribution, we assume access to the full (continuous) data set and propose a tractable and parsimonious self-exciting marked point processes model able to capture the non-Markovian effect of rating momentum. Compared to the Markov model, the non-Markov model yields higher probabilities of default in the investment grades, but also lower default probabilities in some speculative grades. Both findings agree with empirical observations and have clear practical implications.   We illustrate all methods using data from Moody\'s proprietary corporate credit ratings data set. Implementations are available in the R package ctmcd.
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中文摘要：
我们提出了两种在马尔可夫和非马尔可夫框架下估计评级转移概率的方法。我们首先使用离散（缺失）数据估计连续时间马尔可夫链，并导出Fisher信息矩阵的更简单表达式，从而将Wald置信区间所需的计算时间减少一半。我们提供了一种有效的方法，将这种不确定性从马尔可夫链的生成矩阵转移到相应的评级迁移概率，以及关键的违约概率。对于我们的第二个贡献，我们假设可以访问完整的（连续的）数据集，并提出了一个易于处理且简洁的自激标记点过程模型，该模型能够捕捉评级动量的非马尔可夫效应。与马尔可夫模型相比，非马尔可夫模型在投资等级中的违约概率较高，但在某些投机等级中的违约概率也较低。这两个发现都与经验观察结果一致，并具有明确的实际意义。我们使用穆迪专有公司信用评级数据集的数据来说明所有方法。R包ctmcd中提供了实现。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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