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Forecasting Fed Cattle, Feeder Cattle, and Corn Cash Price Volatility: The Accuracy of Time Series, Implied Volatility, and Composite Approaches

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  • Manfredo, Mark R.
  • Leuthold, Raymond M.
  • Irwin, Scott H.

Abstract

Economists and others need estimates of future cash price volatility to use in risk management evaluation and education programs. This paper evaluates the performance of alternative volatility forecasts for fed cattle, feeder cattle, and corn cash price returns. Forecasts include time series (e.g. GARCH), implied volatility from options on futures contracts, and composite specifications. The overriding finding from this research, consistent with the existing volatility forecasting literature, is that no single method of volatility forecasting provides superior accuracy across alternative data sets and horizons. However, evidence is provided suggesting that risk managers and extension educators use composite methods when both time series and implied volatilities are available.

Suggested Citation

  • Manfredo, Mark R. & Leuthold, Raymond M. & Irwin, Scott H., 2001. "Forecasting Fed Cattle, Feeder Cattle, and Corn Cash Price Volatility: The Accuracy of Time Series, Implied Volatility, and Composite Approaches," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 33(3), pages 523-538, December.
    • Handle: RePEc:cup:jagaec:v:33:y:2001:i:03:p:523-538_02
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    References listed on IDEAS

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    1. Beckers, Stan, 1981. "Standard deviations implied in option prices as predictors of future stock price variability," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 363-381, September.
    2. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    3. 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.
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    Cited by:

    1. Benavides, Guillermo & Capistrán, Carlos, 2012. "Forecasting exchange rate volatility: The superior performance of conditional combinations of time series and option implied forecasts," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 627-639.
    2. Benavides Guillermo, 2010. "Forecasting Short-Run Inflation Volatility using Futures Prices: An Empirical Analysis from a Value at Risk Perspective," Working Papers 2010-12, Banco de México.
    3. Bekkerman, Anton & Pelletier, Denis, 2009. "Basis Volatilities of Corn and Soybean in Spatially Separated Markets: The Effect of Ethanol Demand," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49281, Agricultural and Applied Economics Association.
    4. Benavides Guillermo, 2020. "Asymmetric Volatility Effects in Risk Management: An Empirical Analysis using a Stock Index Futures," Working Papers 2020-10, Banco de México.
    5. Guillermo Benavides, 2010. "Forecasting Short-Run Inflation Volatility using Futures Prices: An Empirical Analysis from a Value at Risk Perspective," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 4(2), pages 1-27.
    6. Guillermo Benavides, 2021. "Asymmetric Volatility Relevance in Risk Management: An Empirical Analysis using Stock Index Futures," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(TNEA), pages 1-18, Septiembr.
    7. Rosa, Franco & Vasciaveo, Michela, 2012. "Volatility in US and Italian agricultural markets, interactions and policy evaluation," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122530, European Association of Agricultural Economists.

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