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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, 2006. "Intermediate Volatility Forecasts Using Implied Forward Volatility: The Performance of Selected Agricultural Commodity Options," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 31(3), pages 1-21, December.
    • Handle: RePEc:ags:jlaare:8637
    DOI: 10.22004/ag.econ.8637
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    1. 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.
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    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.

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