Premium
The performance of event study approaches using daily commodity futures returns
Author(s) -
Mckenzie Andrew M.,
Thomsen Michael R.,
Dixon Bruce L.
Publication year - 2004
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.10126
Subject(s) - autoregressive conditional heteroskedasticity , econometrics , futures contract , statistics , ordinary least squares , economics , mathematics , normal distribution , event (particle physics) , null hypothesis , financial economics , volatility (finance) , physics , quantum mechanics
Simulations are conducted to assess the inferential accuracy of statistical event study approaches using daily futures returns. Methods examined include constant mean return models and several regression models—OLS, GARCH(1,1), and a GARCH(1,1) model having an error term with a Student's t distribution. The simulations address four of the most commonly analyzed agricultural futures commodities—corn, soybeans, live cattle, and hogs. In terms of the size of the test statistics, constant mean return models with short normal periods perform poorly, leading to unacceptably high rejection rates of the null hypothesis. Test statistics from constant mean return models with longer normal periods, OLS, and GARCH specifications provide rejection rates largely consistent with those of a unit normal distribution. Test statistics from all models are powerful enough to detect abnormal performance levels below those that would trigger limit locks. At small levels of abnormal performance the GARCH(1,1) model with a t distribution was consistently the most powerful model. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:533–555, 2004