Bootstrap Tests for Multivariate Event Studies

Author: Pin-Huang Chou

Source: Review of Quantitative Finance and Accounting, Volume 23, Number 3, November 2004 , pp. 275-290(16)

Publisher: Springer

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Abstract:

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.

Keywords: bootstrap; multivariate event studies; likelihood ratio test; Wald test; exact test

Document Type: Research article

DOI: http://dx.doi.org/10.1023/B:REQU.0000042345.03125.6f

Affiliations: 1: Department of Finance, National Central University, Chung Li, Taiwan 320, Tel.: 886-3-4227151, Ext. 6270; Fax: 886-3-4252961., Email: choup@cc.ncu.edu.tw

Publication date: 2004-11-01