Event studies with a contaminated estimation period

Parametric and Nonparametric Event Study Tests: A Review

“…Warner (1980, 1985) report that increases in variance may result in misspecification of the traditional test statistics and that the power of tests can be improved by appropriately modeling the volatility process. Other studies such as Aktas et al (2007), Harrington and Shrider (2007) and Higgins and Peterson (1998) also document that all events induce an increase in cross-sectional variance that must be estimated and adjustments embodied in all tests used to assess the statistical significance of event date abnormal returns. Boehmer, Musumeci, and Poulsen (BMP) (1991) argue that the event-period returns should be standardized by the estimation-period standard deviation, and the cross-sectional mean of the standardized returns needs to be divided by their cross-sectional standard deviation to yield the test statistic.…”

Short-horizon event study estimation with a STAR model and real contaminated events

“…1 The most notable recent attempts focus on the introduction of test statistics for nonzero mean abnormal returns that are robust to event-induced increase in return variance (see, for instance, Harrington and Shrider 2007; Kolari and Pennönen 2010 and references therein). 2 More recently, Aktas et al (2007a) emphasize the need to consider event study methods that mitigate the effect of contaminated (unrelated) events arising from corporate actions and announcements that may occur during the estimation window. It is reasonable to expect that company press releases or leakage of private information occurring in the estimation window could create cross-sectional variation in the abnormal returns.…”

“…The majority of prior literature focuses on providing analytical and empirical evidence of the resulting test statistics biases using randomly selected firms with simulated induced contaminated events during the estimation period (e.g., Aktas et al 2007a;Harrington and Shrider 2007). Nevertheless, by carrying out specification and power tests on estimation periods using simulated returns data may not always be representative enough of 4 the resulting cross-sectional variation in abnormal returns emerging from eventcontamination that is taking place in real situations.…”

“…First, we include approaches that have been extensively used in prior empirical studies like the standardized cross-sectional test proposed by Boehmer et al (1991), RANK test proposed by Corrado (1989) and GARCH test proposed by Savickas (2003). Second, we consider methods having greater flexibility to mitigate the presence of firm-specific (unrelated) events that precede corporate announcements like the BETA-1 approach, the two-state Markovswitching market model test (TSMM) proposed by Aktas et al (2007a) and the STAR test 6 statistic introduced in this study. For the TSMM, we do not only consider two-state variance regimes as in Aktas et al (2007a), but we also investigate two-state regimes in the market model parameters (i.e., stock return mean regression equation).…”

“…Second, we consider methods having greater flexibility to mitigate the presence of firm-specific (unrelated) events that precede corporate announcements like the BETA-1 approach, the two-state Markovswitching market model test (TSMM) proposed by Aktas et al (2007a) and the STAR test 6 statistic introduced in this study. For the TSMM, we do not only consider two-state variance regimes as in Aktas et al (2007a), but we also investigate two-state regimes in the market model parameters (i.e., stock return mean regression equation). 5 Unlike their peers, the regime-switching family comprised by the TSMM and STAR models, postulate an adaptation of the event study methodology that automatically takes into consideration the probability of contaminated events that could otherwise bias the estimated parameters of the market model (affecting in this way the specification and power of test statistics).…”