Bootstrap Tests for Multivariate Event Studies
23 Pages Posted: 11 Feb 1999
Date Written: October 1998
Abstract
Statistical tests for multivariate event studies, exact or asymptotic, have been derived based on multivariate normality. As it has been 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 a bootstrap alternative to multivariate event studies that does not require a specific distributional assumption. The Monte Carlo experiments based on 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.
JEL Classification: C13,C53,G14
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
Alternative Methods for Robust Analysis in Event Study Applications
-
By Scott E. Hein and Peter Westfall
-
Conducting Event Studies on a Small Stock Exchange
By Jan Bartholdy, Dennis Olson, ...
-
On the Statistical Significance of Event Effects on Unsystematic Volatility
By Jimmy E. Hilliard and Robert Savickas
-
Conducting Event Studies With Asia-Pacific Security Market Data
By Charles J. Corrado and Cameron Truong
-
By George S. Ford and Audrey D. Kline
-
HAC Standard Errors and the Event Study Methodology: A Cautionary Note
By George S. Ford, John D. Jackson, ...