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Variance Risk Premiums and Predictive Power of Alternative Forward Variances in the Corn Market

Author

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  • Wang, Zhiguang
  • Fausti, Scott W.
  • Qasmi, Bashir A.

Abstract

We propose a fear index for corn using the variance swap rate synthesized from out-of-the-money call and put options as a measure of implied variance. Previous studies estimate implied variance based on Black (1976) model or forecast variance using the GARCH models. Our implied variance approach, based on variance swap rate, is model independent. We compute the daily 60-day variance risk premiums based on the difference between the realized variance and implied variance for the period from 1987 to 2009. We find negative and time-varying variance risk premiums in the corn market. Our results contrast with Egelkraut, Garcia, and Sherrick (2007), but are in line with the findings of Simon (2002). We conclude that our synthesized implied variance contains superior information about future realized variance relative to the implied variance estimates based on the Black (1976) model and the variance forecasted using the GARCH(1,1) model.

Suggested Citation

  • Wang, Zhiguang & Fausti, Scott W. & Qasmi, Bashir A., 2010. "Variance Risk Premiums and Predictive Power of Alternative Forward Variances in the Corn Market," Economics Staff Papers 61683, South Dakota State University, Department of Economics.
    • Handle: RePEc:ags:sdsusp:61683
    DOI: 10.22004/ag.econ.61683
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    Cited by:

    1. Bakas, Dimitrios & Triantafyllou, Athanasios, 2018. "The impact of uncertainty shocks on the volatility of commodity prices," Journal of International Money and Finance, Elsevier, vol. 87(C), pages 96-111.
    2. Athanasios Triantafyllou & George Dotsis & Alexandros Sarris, 2020. "Assessing the Vulnerability to Price Spikes in Agricultural Commodity Markets," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 631-651, September.
    3. Yabei Zhu & Xingguo Luo & Qi Xu, 2023. "Industry variance risk premium, cross‐industry correlation, and expected returns," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(1), pages 3-32, January.
    4. Athanasios Triantafyllou & Dimitrios Bakas & Marilou Ioakimidis, 2023. "Commodity price uncertainty as a leading indicator of economic activity," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4194-4219, October.
    5. 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.
    6. Trujillo-Barrera, Andres & Mallory, Mindy L. & Garcia, Philip, 2012. "Volatility Spillovers in U.S. Crude Oil, Ethanol, and Corn Futures Markets," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 37(2), pages 1-16, August.
    7. Triantafyllou, Athanasios & Dotsis, George, 2017. "Option-implied expectations in commodity markets and monetary policy," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 1-17.
    8. Lyu, Yongjian & Tuo, Siwei & Wei, Yu & Yang, Mo, 2021. "Time-varying effects of global economic policy uncertainty shocks on crude oil price volatility：New evidence," Resources Policy, Elsevier, vol. 70(C).

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    More about this item

    Keywords

    Risk and Uncertainty;

    JEL classification:

    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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