There have been several attempts to address these statistical issues. If this impact is igorned, the test statistic becomes inflated (in absoluate value), leading to liberal inference: If the null hypothesis is true, it will be rejected with probability greater than the nominal signficance level of the test that is, there is an unduly large chance to `find' something in the data, even though nothing happened in reality. As a consequence, both issues impact the standard error which appears in the numerator of a t-test statistic (that is, of the test statistic of a parametric test). Event-induced changes of volatility, on the other hand, is a phenomenon common to many event types (e.g., M&A transactions) that becomes problematic when events are clustered. A cross-sectional correlation arises when sample studies focus on (an) event(s) that happened for multiple firms at the same day(s). Specifically, event-date clustering poses a problem leading to (i) cross-sectional correlation of abnormal returns and (ii) distortions from event-induced volatility changes. Generalized Sign Test, Generalized RankВ T Test, and Generalized RankВ ZВ TestДЄmong the most widely used parametric tests are those developed by Patell (1976) and Boehmer, Musumeci and Poulsen (1991), whereas among the most widely used nonparametric tests are the rank-test of Corrado (1989), and the sign-test of Cowan (1992).ДЄn informed choice of test statistic should be based on the research setting and the statistical issues pertaining to the observed data. Generalized Sign Test, Generalized Rank TВ Test, and Generalized RankВ ZВ TestД¬ross-SectionalВ Test, Time-Series Standard Deviation Test, Patell Test, AdjustedВ Patell Test, Standardized Cross-Sectional Test, Adjusted Standardized Cross-Sectional Test, and Skewness Corrected Test Table 1: Significance tests Null hypothesisД¬ross-SectionalВ Test, Crude Dependence Adjustment Test, Patell Test, AdjustedВ Patell Test, Standardized Cross-Sectional Test, Adjusted Standardized Cross-Sectional Test, and Skewness Corrected Test Table 1 provides an overview toghether with links to the formulas of the different test statistics.

Applied researches typically carray out both parametric and nonparametric tests to verify that the research findings are not driven by outliers, which tend to affect the results of parametric tests but not the results of nonparametric tests for example, see Schipper and Smith (1983).

Parametric tests assume that the individual firm's abnormal returns are normally distributed, whereas nonparametric tests do not rely on any such assumptions.

Generally, significance tests can be classified into parametricВ and nonparametric tests. The literature on event-study hypthesis testing covers a wide range of tests and is thus very comprehensive. egin$ denotes the forecast-error-corrected variance. Formally, the testing framework reads as follows: The answer aboutВ statistical significance is givenВ by means of hypothesis testing, whereВ the null hypothesis ($H_0$) states that the mean of the abnormal returnsВ within the event window is zero and the alternative hypothesis ($H_1$) states the opposite. In the relevant literature, the focus is almost always on the mean of the distrubtion of abnormal returns and, specifically, one seeks to answer the questions whether this mean is different from zero (with statistical significance). Either they are used as dependent variables in subsequent regressionВ analyses or they are interpreted as such.В This latterВ direct interpretation seeksВ to answer the question whether the distribution of the abnormal returns is systematically different from predicted. The abnormal and cumulative abnormal returnsВ from event studies are typically used in two ways.

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