摘要翻译：
PD曲线校准是指将一组评级等级水平违约概率(PDs)转换为另一个平均PD水平，该水平由基础投资组合的PD变化决定。本文提出了一个框架，允许探索各种校准方法和条件下，他们适合的目的。我们通过将讨论的方法应用于可以被认为是方法适用范围典型的机构评级和违约统计的公开数据集来测试它们。我们表明流行的“标度PDS”方法在理论上是有问题的，并确定了一种替代的校准方法（“标度似然比”），它在理论上是合理的，在测试数据集上表现更好。关键词：违约概率，校准，似然比，贝叶斯公式，评级剖面，二元分类。
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英文标题：
《The art of probability-of-default curve calibration》
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作者：
Dirk Tasche
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最新提交年份：
2013
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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：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  PD curve calibration refers to the transformation of a set of rating grade level probabilities of default (PDs) to another average PD level that is determined by a change of the underlying portfolio-wide PD. This paper presents a framework that allows to explore a variety of calibration approaches and the conditions under which they are fit for purpose. We test the approaches discussed by applying them to publicly available datasets of agency rating and default statistics that can be considered typical for the scope of application of the approaches. We show that the popular 'scaled PDs' approach is theoretically questionable and identify an alternative calibration approach ('scaled likelihood ratio') that is both theoretically sound and performs better on the test datasets.   Keywords: Probability of default, calibration, likelihood ratio, Bayes' formula, rating profile, binary classification.
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PDF链接：
https://arxiv.org/pdf/1212.3716