Measuring Abnormal Bond Performance
分析公司事件研究中检测债券异常收益的统计检验效力与设定，发现常用月度方法有偏且效力低，而使用日度数据能显著提高检验效力。
We analyze the empirical power and specification of test statistics designed to detect abnormal bond returns in corporate event studies, using monthly and daily data. We find that test statistics based on frequently used methods of calculating abnormal monthly bond returns are biased. Most methods implemented in monthly data also lack power to detect abnormal returns. We also consider unique issues arising when using the newly available daily bond data, and formulate and test methods to calculate daily abnormal bond returns. Using daily bond data significantly increases the power of the tests, relative to the monthly data. Weighting individual trades by size while eliminating noninstitutional trades from the TRACE data also increases the power of the tests to detect abnormal performance, relative to using all trades or the last price of the day. Further, value-weighted portfolio-matching approaches are better specified and more powerful than equal-weighted approaches. Finally, we examine abnormal bond returns to acquirers around mergers and acquisitions to demonstrate how the abnormal return model and use of daily versus monthly data can affect inferences. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.