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Intermediate Volatility Forecasts Using Implied Forward Volatility: The Performance of Selected Agricultural Commodity Options

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  • Egelkraut, Thorsten M.
  • Garcia, Philip
Abstract
Options with different maturities can be used to generate an implied forward volatility, a volatility forecast for non-overlapping future time intervals. Using five commodities with varying characteristics, we find that the implied forward volatility dominates forecasts based on historical volatility information, but that the predictive accuracy is affected by the commodity's characteristics. Unbiased and efficient corn and soybeans market forecasts are attributable to the well-established volatility during crucial growing periods. For soybean meal, wheat, and hogs volatility is less predictable, and investors appear to demand a risk premium for bearing volatility risk.

Suggested Citation

  • Egelkraut, Thorsten M. & Garcia, Philip, 2005. "Intermediate Volatility Forecasts Using Implied Forward Volatility: The Performance of Selected Agricultural Commodity Options," 2005 Conference, April 18-19, 2005, St. Louis, Missouri 19033, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    • Handle: RePEc:ags:ncrfiv:19033
    DOI: 10.22004/ag.econ.19033
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    File URL: https://ageconsearch.umn.edu/record/19033/files/cp05eg02.pdf
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    References listed on IDEAS

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    1. Thorsten M. Egelkraut & Philip Garcia & Bruce J. Sherrick, 2007. "The Term Structure of Implied Forward Volatility: Recovery and Informational Content in the Corn Options Market," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(1), pages 1-11.
    2. Barone-Adesi, Giovanni & Whaley, Robert E, 1987. "Efficient Analytic Approximation of American Option Values," Journal of Finance, American Finance Association, vol. 42(2), pages 301-320, June.
    3. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    6. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    7. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
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    Cited by:

    1. Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas, 2022. "Forecasting realized volatility of agricultural commodities," International Journal of Forecasting, Elsevier, vol. 38(1), pages 74-96.
    2. Andres Trujillo-Barrera & Philip Garcia & Mindy L Mallory, 2018. "Short-term price density forecasts in the lean hog futures market," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(1), pages 121-142.
    3. Brittain, Lee & Garcia, Philip & Irwin, Scott H., 2011. "Live and Feeder Cattle Options Markets: Returns, Risk, and Volatility Forecasting," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 36(1), pages 1-20, April.
    4. Adjemian, Michael K. & Bruno, Valentina G. & Robe, Michel A., 2016. "Forward‐Looking USDA Price Forecasts," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235931, Agricultural and Applied Economics Association.
    5. Giovanni Campisi & Silvia Muzzioli, 2021. "Designing volatility indices for Austria, Finland and Spain," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 369-455, September.
    6. Matteo Bonato & Oğuzhan Çepni & Rangan Gupta & Christian Pierdzioch, 2023. "El Niño, La Niña, and forecastability of the realized variance of agricultural commodity prices: Evidence from a machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 785-801, July.
    7. Adrian Fernandez‐Perez & Bart Frijns & Ilnara Gafiatullina & Alireza Tourani‐Rad, 2019. "Properties and the predictive power of implied volatility in the New Zealand dairy market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(5), pages 612-631, May.
    8. Guimaraes, Jonathan S. & Cruz, Jose Cesar, 2017. "Future volatility forecast in agricultural commodity markets," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258480, Agricultural and Applied Economics Association.

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