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

Published online by Cambridge University Press:  12 June 2017

Mark R. Manfredo
Affiliation:
Morrison School of Agribusiness and Resource Management atArizona State University
Raymond M. Leuthold
Affiliation:
Office for Futures and Options Research
Scott H. Irwin
Affiliation:
Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign

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.

Type
Articles
Copyright
Copyright © Southern Agricultural Economics Association 2001

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References

Anderson, T.G. and Bollerslev, T.Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts.” International Economic Review 39(1998):885905.Google Scholar
Beckers, S.Standard Deviations Implied in Option Prices as Predictors of Future Stock Price Variability.” Journal of Banking and Finance 5(1981):363382.CrossRefGoogle Scholar
Berndt, E.K., Hall, B.H., Hall, R.E., and Hausman, J.A.Estimation Inference in Nonlinear Structural Models.” Annals of Economic and Social Measurement 3/4(1974):653665.Google Scholar
Black, F.The Pricing of Commodity Contracts.” Journal of Financial Economics 3(1976): 167179.Google Scholar
Boehlje, M.D. and Lins, D.A.Risks and Risk Management in an Industrialized Agriculture.” Agricultural Finance Review 58(1998):116.Google Scholar
Bollerslev, T., Chou, R.Y., and Kroner, K.E. “ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence.” Journal of Econometrics 52(1992):559.Google Scholar
Boudoukh, J., Richardson, M., and Whitelaw, R.E. “Investigation of a Class of Volatility Estimators.” The Journal of Derivatives 4(1997):6371.Google Scholar
Brailsford, T.J. and Faff, R.W.An Evaluation of Volatility Forecasting Techniques.” Journal of Banking and Finance 20(1996):419438.CrossRefGoogle Scholar
Campbell, J.Y., Lo, A.W., and MacKinlay, A.C. The Econometrics of Financial Markets. Princeton NJ: Princeton University Press, 1997.Google Scholar
Christoffersen, P.F., Diebold, F.X.How Relevant is Volatility Forecasting for Financial Risk Management?Review of Economics and Statistics 82(2000):111.Google Scholar
Christoffersen, P.F., Diebold, F.X., and Schuer-mann, T.Horizon Problems and Extreme Events in Financial Risk Management.” Economic Policy Review, Federal Reserve Bank of New York 4(1998):109118.Google Scholar
Clemen, R.T.Combining Forecasts: A Review and Annotated Bibliography.” International Journal of Forecasting 5(1989):559583.CrossRefGoogle Scholar
Day, T.E. and Lewis, C.M.Stock Market Volatility and the Information Content of Stock Index Options.” Journal of Econometrics 52(1992):267287.CrossRefGoogle Scholar
Day, T.E. and Lewis, C.M.Forecasting Futures Market Volatility.” The Journal of Derivatives 1(1993):3350.Google Scholar
Diebold, E.X., Hickman, A., Inoue, A., and Schuermann, T.Converting 1-Day Volatility to h-Day Volatility: Scaling by is Worse than You Think.” Wharton Financial Institutions Working Paper 97-34, University of Pennsylvania, 1-16, 1997.Google Scholar
Diebold, F.X. and Mariano, R.S.Comparing Predictive Accuracy.” Journal of Business and Economic Statistics 13(1995):253263.Google Scholar
Figlewski, S.Forecasting Volatility.” Financial Markets, Institutions, and Instruments 6(1997):287.Google Scholar
Granger, C.W.J., and Ramanathan, R.Improved Methods of Combining Forecasts.” Journal of Forecasting 3(1984):197204.Google Scholar
Harvey, D., Leybourne, S., and Newbold, P.Testing the Equality of Prediction Mean Squared Errors.” International Journal of Forecasting 13(1997):281291.Google Scholar
Jackson, P., Maude, D.J., and Perraudin, W.Bank Capital and Value at Risk.” The Journal of Derivatives 4(1997):73–89.Google Scholar
Jones, R., Mintert, J., Langemeier, M., Schroeder, T., and Albright, M.Sources of Economic Variability in Cattle Feeding.” Proceedings of the NCR-134 Conference on Applied Commodity Forecasting and Risk Management, Chicago, April 1996, pp. 336-348.Google Scholar
Jorion, P.Predicting Volatility in the Foreign Exchange Market.” The Journal of Finance 50(1995):507528.Google Scholar
Kroner, K.F., Kneafsey, K.P., and Claessens, S.Forecasting Volatility in Commodity Markets.” Journal of Forecasting 14(1994):7795.Google Scholar
Lamoureux, C.G. and Lastrapes, W.D.Forecasting Stock Return Variance: Toward an Understanding of Stochastic Implied Volatilities.” The Review of Financial Studies 6(1993):293326.Google Scholar
Mahoney, J. M. “Empirical-based versus Model-based Approaches to Value-at-Risk: An Examination of Foreign Exchange and Global Equity Portfolios.” Proceedings of a Joint Central Bank Research Conference, Board of Governors of the Federal Reserve System, 1996, pp. 199217.Google Scholar
Makridakis, S.Why Combining Works?International Journal of Forecasting 5(1989):601603.Google Scholar
Martin, D.R., H-Y, Gao, Zhan, Y., and Ding, Z., S+GARCH Users Manual, Seattle, WA: The MathSoft Corporation, 1996.Google Scholar
Mayhew, S.Implied Volatility.” Financial Analysts Journal 54(1995):819.Google Scholar
Miles, R. Oklahoma City Agricultural Marketing Service. Personal Communication. December 1997.Google Scholar
Park, D.W. and Tomek, W.G.An Appraisal of Composite Forecasting Methods.” North Central Journal of Agricultural Economics 10(1989):111.Google Scholar
RiskMetrics™ Technical Document 4th Edition, Morgan Guaranty Trust Company, New York, http://www.RiskMetrics.com/research, 1996.Google Scholar
Rob, J., Livestock Marketing Information Center. Personal Communication. December 1997.Google Scholar
Schroeder, T.C, Albright, M.L., Langemeier, M.R., and Mintert, J.Factors Affecting Cattle Feeding Profitability.” Journal of the American Society of Farm Managers and Rural Appraisers 57(1993):4854.Google Scholar
Shastri, K. and Tandon, K.On the Use of European Models to Price American Options on Foreign Currency.” Journal of Futures Markets 6(1986):93108.Google Scholar
Whaley, R.Valuation of American Futures Options: Theory and Empirical Tests.” Journal of Finance 41(1986):127150.Google Scholar
West, K.D., and Cho, D.The Predictive Ability of Several Models of Exchange Rate Volatility.” Journal of Econometrics 69(1995):367391.Google Scholar
Yang, S. and Brorsen, B.W.Nonlinear Dynamics of Daily Cash Prices.” American Journal of Agricultural Economics 74(1992):707715.Google Scholar