Publication details for Professor Panayiotis AndreouAndreou, P., Louca, C. & Savva, C. S. (2016). Short‐Horizon Event Study Estimation with a STAR Model and Real Contaminated Events. Review of Quantitative Finance and Accounting 47(3): 673-697.
- Publication type: Journal Article
- ISSN/ISBN: 0924-865X, 1573-7179
- DOI: 10.1007/s11156-015-0515-3
- Keywords: Event studies, Test statistics, Contaminated events, Markov switching regression model, Smooth Transition Auto Regressive model.
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
We propose a test statistic for nonzero mean abnormal returns based on a Smooth Transition Auto Regressive (STAR) model specification. Estimation of STAR takes into account the probability of contaminated events that could otherwise bias the parameters of the market model and thus the specification and power of the test statistic. Using both simulated and real stock returns data from mergers and acquisitions, we find that the STAR test statistic is robust to contaminated events occurring in the estimation window and in the presence of event-induced increase in return variance. Under the STAR test statistic the true null hypothesis is rejected at appropriate levels. Moreover, it exhibits the highest levels of power when compared with other test statistics that are widely and routinely applied in short-horizon event studies.