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The performance of event study approaches using daily commodity futures returns

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  • Andrew M. Mckenzie
  • Michael R. Thomsen
  • Bruce L. Dixon

Abstract

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

Suggested Citation

  • Andrew M. Mckenzie & Michael R. Thomsen & Bruce L. Dixon, 2004. "The performance of event study approaches using daily commodity futures returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(6), pages 533-555, June.
    • Handle: RePEc:wly:jfutmk:v:24:y:2004:i:6:p:533-555
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    1. Karali, Berna & Isengildina-Massa, Olga & Irwin, Scott H. & Adjemian, Michael K. & Johansson, Robert, 2019. "Are USDA reports still news to changing crop markets?," Food Policy, Elsevier, vol. 84(C), pages 66-76.
    2. Humphery-Jenner, M., 2011. "Internal and External Discipline Following Securities Class Actions," Other publications TiSEM 9bcb5c91-4bab-431f-9891-1, Tilburg University, School of Economics and Management.
    3. Humphery-Jenner, M., 2011. "Internal and External Discipline Following Securities Class Actions," Other publications TiSEM 072318eb-d214-4c7a-ac7a-d, Tilburg University, School of Economics and Management.
    4. Kishore Joseph & Philip Garcia, 2018. "Intraday market effects in electronic soybean futures market during non-trading and trading hour announcements," Applied Economics, Taylor & Francis Journals, vol. 50(11), pages 1188-1202, March.
    5. Jia, Jun-Jun & Xu, Jin-Hua & Fan, Ying, 2016. "The impact of verified emissions announcements on the European Union emissions trading scheme: A bilaterally modified dummy variable modelling analysis," Applied Energy, Elsevier, vol. 173(C), pages 567-577.
    6. McKenzie, Andrew M. & Ke, Yangmin, 2022. "How do USDA announcements affect international commodity prices?," Journal of Commodity Markets, Elsevier, vol. 28(C).
    7. Sharon Kozicki & Eric Santor & Lena Suchanek, 2015. "Large-Scale Asset Purchases: Impact on Commodity Prices and International Spillover Effects," Staff Working Papers 15-21, Bank of Canada.
    8. Gang Wang, 2019. "The Effects of Quantitative Easing Announcements on the Mortgage Market: An Event Study Approach," IJFS, MDPI, vol. 7(1), pages 1-30, February.
    9. Rommel, J. & Neuenfeldt, S. & Odening, M., 2010. "Markteffekte medienwirksamer Lebensmittelskandale – eine Ergebnisstudie," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 45, March.
    10. Yoo, Kyungjin & Lee, Youah & Heo, Eunnyeong, 2013. "Economic effects by merger and acquisition types in the renewable energy sector: An event study approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 694-701.

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