Improving Tests of Abnormal Returns by Bootstrapping the Multivariate Regression Model with Event Parameters

Parametric dummy variable-based tests for event studies using multivariate regression are not robust to nonnormality of the residual, even for arbitrarily large sample sizes. Bootstrap alternatives are described, investigated, and compared for cases where there are nonnormalities, and cross-sectional and time-series dependencies. Independent bootstrapping of residual vectors from the multivariate regression model controls type I error rates in the presence of cross-sectional correlation, and surprisingly, even in the presence of time-series dependence structures. The proposed methods not only improve upon parametric methods, but also allow development of new and powerful event study tests for which there is no parametric counterpart. Copyright 2004, Oxford University Press.