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Risk-adjusted implied volatility and its performance in forecasting realized volatility in corn futures prices

Author

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  • Wu, Feng
  • Myers, Robert J.
  • Guan, Zhengfei
  • Wang, Zhiguang
Abstract
We propose a methodology for constructing a risk-adjusted implied volatility measure that removes the forecast bias of model-free implied volatility that is typically believed to be related to risk premiums. The risk adjustment is based on a generalized, closed-form relationship between the expectation of future volatility and the model-free implied volatility assuming a jump-diffusion model. We also develop a GMM framework to estimate key model parameters. An empirical application using corn futures and option prices is used to illustrate the methodology and demonstrate differences between our approach and the standard model-free implied volatility. We compare the risk-adjusted forecast with the unadjusted forecast as well as other alternatives. Results suggest that the risk-adjusted volatility is unbiased, informationally efficient, and has superior predictive power over the alternatives considered.

Suggested Citation

  • Wu, Feng & Myers, Robert J. & Guan, Zhengfei & Wang, Zhiguang, 2015. "Risk-adjusted implied volatility and its performance in forecasting realized volatility in corn futures prices," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 260-274.
    • Handle: RePEc:eee:empfin:v:34:y:2015:i:c:p:260-274
    DOI: 10.1016/j.jempfin.2015.07.003
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    Cited by:

    1. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    2. repec:ags:aaea22:335789 is not listed on IDEAS
    3. Qiao, Gaoxiu & Teng, Yuxin & Li, Weiping & Liu, Wenwen, 2019. "Improving volatility forecasting based on Chinese volatility index information: Evidence from CSI 300 index and futures markets," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 133-151.
    4. Feng, Jiabao & Wang, Yudong & Yin, Libo, 2017. "Oil volatility risk and stock market volatility predictability: Evidence from G7 countries," Energy Economics, Elsevier, vol. 68(C), pages 240-254.
    5. Anabelle Couleau & Teresa Serra & Philip Garcia, 2020. "Are Corn Futures Prices Getting “Jumpy”?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(2), pages 569-588, March.

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    More about this item

    Keywords

    Volatility risk premium; Model-free implied volatility; Diffusion jump; GMM estimation;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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