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. In a spirit similar to that of Brown and Warner (1980, 1985) and Bessembinder et al. (2009), we follow a simulation approach to examine the statistical properties of normal and abnormal CDS spreads and assess the performance of normal return models and test statistics. Using daily CDS data, we find that parametric test statistics are generally inferior to non-parametric tests, with the rank test performing best. Some of the classical normal return models, such as the market model, are found to be poorly specified. A CDS factor model based on factors identified in the empirical literature is generally well specified and more powerful in detecting abnormal performance. If factor information is not available, a simple mean-adjusted approach should be used. Finally, we examine performance in the presence of event-induced variance increases and bootstrapped p-values. Our inferences hold for US and European CDS data and are not affected by reference entity credit quality.
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