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The forecasting performance of implied volatility from live cattle options contracts: Implications for agribusiness risk management

Author

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  • Mark R. Manfredo

    (Morrison School of Agribusiness and Resource Management, Arizona State University, East 7001 E. Williams Field Rd., Wanner Hall, Mesa, AZ 85212. E-mail: manfredo@asu.edu)

  • Dwight R. Sanders

    (Department of Agribusiness Economics, Southern Illinois University, Mail code 4410, Carbondale, IL 62901-4410. E-mail: dwights@siu.edu)

Abstract

This research examines the forecasting performance of implied volatility derived from nearby live cattle options contracts in predicting 1-week volatility of nearby live cattle futures prices. Forecast evaluation is conducted from the perspective of an agribusiness risk manager. The methodology employed avoids overlapping forecast horizons and focuses on forecast errors, minimizing interpretive issues. Results suggest that implied volatility is a biased and inefficient forecast of 1-week nearby live cattle futures price volatility. However, implied volatility encompasses all information provided by a time series alternative, and it has improved as a forecast over time. These findings provide insight to agribusiness risk managers on how to adjust for bias and inefficiency of implied volatility, and provide insight into their information content. [JEL|EconLit citations: Q130, Q140, G130.] © 2004 Wiley Periodicals, Inc. Agribusiness 20: 217-230, 2004.

Suggested Citation

  • Mark R. Manfredo & Dwight R. Sanders, 2004. "The forecasting performance of implied volatility from live cattle options contracts: Implications for agribusiness risk management," Agribusiness, John Wiley & Sons, Ltd., vol. 20(2), pages 217-230.
    • Handle: RePEc:wly:agribz:v:20:y:2004:i:2:p:217-230
    DOI: 10.1002/agr.20003
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    References listed on IDEAS

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    1. Jorion, Philippe, 1995. "Predicting Volatility in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 50(2), pages 507-528, June.
    2. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    3. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    4. Henry J. Aaron, 2000. "Presidential address- Seeing through the fog," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 193-206.
    5. Marvin L. Hayenga & Bingrong Jiang & Sergio H. Lence, 1996. "Improving wholesale beef and pork product cross hedging," Agribusiness, John Wiley & Sons, Ltd., vol. 12(6), pages 541-559.
    6. Bailey, DeeVon & Brorsen, B. Wade, 1998. "Trends In The Accuracy Of Usda Production Forecasts For Beef And Pork," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 23(2), pages 1-11, December.
    7. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    8. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    9. 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.
    10. Mark R. Manfredo & Raymond M. Leuthold, 2001. "Market risk and the cattle feeding margin: An application of Value-at-Risk," Agribusiness, John Wiley & Sons, Ltd., vol. 17(3), pages 333-353.
    11. 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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    2. Glynn Tonsor & Ted Schroeder, 2011. "Multivariate forecasting of a commodity portfolio: application to cattle feeding margins and risk," Applied Economics, Taylor & Francis Journals, vol. 43(11), pages 1329-1339.
    3. Viteva, Svetlana & Veld-Merkoulova, Yulia V. & Campbell, Kevin, 2014. "The forecasting accuracy of implied volatility from ECX carbon options," Energy Economics, Elsevier, vol. 45(C), pages 475-484.
    4. 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.
    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.

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