This paper examines the size and power of test statistics designed to detect abnormal changes in credit risk as measured by credit default swap (CDS) spreads. We follow a simulation approach to examine the statistical properties of normal and abnormal CDS spread changes and assess the performance of normal return models and test statistics. Using daily CDS data, we find parametric test statistics to be generally inferior to non-parametric tests, with the rank test performing best. A CDS factor model based on factors identified in the empirical literature is generally well specified and more powerful in detecting abnormal performance than some of the classical normal return models. Finally, we examine abnormal CDS announcement spread changes around issuer's rating downgrades to demonstrate the effect of different CDS spread change measures and normal return models on event study results.

本文研究了旨在检测​​信用风险异常变化的测试统计量的规模和功效，以信用违约掉期 (CDS) 利差衡量。我们采用模拟方法来检查正常和异常 CDS 价差变化的统计特性，并评估正常回报模型和测试统计数据的性能。使用每日 CDS 数据，我们发现参数检验统计量通常不如非参数检验，秩检验表现最好。基于经验文献中确定的因子的 CDS 因子模型通常被很好地指定，并且在检测异常性能方面比一些经典的正常回报模型更强大。最后，我们检查了发行人周围的异常 CDS 公告价差变化