Bootstrap Tests for Multivariate Event Studies

Bootstrap Tests for Multivariate Event Studies Statistical tests for multivariate event studies—exact or asymptotic—are derived based on multivariate normality. As it has been previously documented that the performances of these tests are not satisfactory, because stock returns are far from normally distributed (especially for daily returns), this paper proposes the use of bootstrap methods, which are free from any specific distributional assumption, to provide better approximations to the sampling distributions of test statistics in multivariate event studies. The Monte Carlo experiments based on real daily returns data show that the bootstrap tests outperform the traditional tests by having close rejection rates to the nominal significance levels. The traditional tests, in contrast, tend to reject the null hypotheses too often. Review of Quantitative Finance and Accounting Springer Journals

Bootstrap Tests for Multivariate Event Studies

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Kluwer Academic Publishers
Copyright © 2004 by Kluwer Academic Publishers
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
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