英文标题：
《Sparse Structural Approach for Rating Transitions》
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作者：
Volodymyr Perederiy
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最新提交年份：
2020
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英文摘要：
  In banking practice, rating transition matrices have become the standard approach of deriving multi-year probabilities of default (PDs) from one-year PDs, the latter normally being available from Basel ratings. Rating transition matrices have gained in importance with the newly adopted IFRS 9 accounting standard. Here, the multi-year PDs can be used to calculate the so-called expected credit losses (ECL) over the entire lifetime of relevant credit assets. A typical approach for estimating the rating transition matrices relies on calculating empirical rating migration counts and frequencies from rating history data. For small portfolios, however, this approach often leads to zero counts and high count volatility, which makes the estimations unreliable and unstable, and can also produce counter-intuitive prediction patterns such as non-parallel/crossing forward PD patterns. This paper proposes a structural model which overcomes these problems. We make a plausible assumption of an underlying autoregressive mean-reverting ability-to-pay process. With only three parameters, this sparse process can well describe an entire typical rating transition matrix, provided the one-year PDs of the rating classes are specified. The transition probabilities produced by the structural approach are well-behaved by design. The approach significantly reduces the statistical degrees of freedom of the estimated transition probabilities, which makes the rating transition matrix more reliable for small portfolios. The approach can be applied to data with as few as 50 observed rating transitions. Moreover, the approach can be efficiently applied to data consisting of continuous PDs (prior to rating discretization). In the IFRS 9 context, the approach offers an additional merit: it can easily account for the macroeconomic adjustments, which are required by the IFRS 9 accounting standard.
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中文摘要：
在银行实践中，评级转换矩阵已成为从一年期违约概率（PDs）推导多年期违约概率（PDs）的标准方法，后者通常可从巴塞尔评级中获得。随着新采用的IFRS 9会计准则，评级转换矩阵变得越来越重要。在此，多年期PDs可用于计算相关信贷资产整个生命周期内的所谓预期信贷损失（ECL）。估计评级转移矩阵的典型方法依赖于根据评级历史数据计算经验评级迁移计数和频率。然而，对于小型投资组合，这种方法通常会导致零计数和高计数波动率，这使得估计不可靠和不稳定，还可能产生反直觉的预测模式，如非平行/交叉前向PD模式。本文提出了一种克服这些问题的结构模型。我们对潜在的自回归均值回复支付能力过程做出了合理的假设。只有三个参数，只要指定了评级类别的一年PDs，该稀疏过程就可以很好地描述整个典型的评级转换矩阵。结构方法产生的转移概率在设计中表现良好。该方法显著降低了估计转移概率的统计自由度，这使得评级转移矩阵对于小型投资组合更加可靠。该方法可应用于观察到的评级转换次数不超过50次的数据。此外，该方法可以有效地应用于由连续PD组成的数据（在评级离散化之前）。在《国际财务报告准则第9号》的背景下，该方法还有一个优点：它可以很容易地解释《国际财务报告准则第9号》会计准则所要求的宏观经济调整。
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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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