Bootstrap tests for multivariate event studies

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20 Scopus citations


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.

Original languageEnglish
Pages (from-to)275-290
Number of pages16
JournalReview of Quantitative Finance and Accounting
Issue number3
StatePublished - Nov 2004


  • Wald test
  • bootstrap
  • exact test
  • likelihood ratio test
  • multivariate event studies


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