Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-20T03:24:58.812Z Has data issue: false hasContentIssue false

Economic Criteria for Evaluating Commodity Price Forecasts

Published online by Cambridge University Press:  28 April 2015

Jeffrey H. Dorfman
Affiliation:
Department of Agricultural and Applied Economics at the University of Georgia
Christopher S. Mcintosh
Affiliation:
Department of Agricultural and Applied Economics at the University of Georgia

Abstract

Forecasts of economic time series are often evaluated according to their accuracy as measured by either quantitative precision or qualitative reliability. We argue that consumers purchase forecasts for the potential utility gains from utilizing them, not for their accuracy. Using Monte Carlo techniques to incorporate the temporal heteroskedasticity inherent in asset returns, the expected utility of a set of qualitative forecasts is simulated for corn and soybean futures prices. Monetary values for forecasts of various reliability levels are derived. The method goes beyond statistical forecast evaluation, allowing individuals to incorporate their own utility function and trading system into valuing a set of asset price forecasts.

Type
Articles
Copyright
Copyright © Southern Agricultural Economics Association 1998

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Becker, G.S.A Theory of the Allocation of Time.” Econ. J. 75(1965):493517.Google Scholar
Bollerslev, T.Generalized Autoregressive Conditional Heteroskedasticity.” J. Econometrics 31(1986):307-27.CrossRefGoogle Scholar
Bollerslev, T., Engle, R.F., and Woolridge, J.M.. “A Capital Asset Pricing Model with Time Varying Covariances.” J. Polit. Econ. 96(1988):116-31.CrossRefGoogle Scholar
Brandt, J.A., and Bessler, D.A.. “Composite Forecasting: An Application with U.S. Hog Prices.” Amer. J. Agr. Econ. 63(1981):135-40.CrossRefGoogle Scholar
Brandt, J.A., and Bessler, D.A.. “Price Forecasting and Evaluation: An Application to Agriculture.” J. Forecasting 2(1983):237-48.CrossRefGoogle Scholar
Brock, W., Lakonishok, J., and LeBaron, B.. “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” J. Finance 47(1992):173164.CrossRefGoogle Scholar
Engle, R.F., Lilien, D.M., and Robins, R.P.. “Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model.” Econometrica 55(1987):391407.CrossRefGoogle Scholar
Fama, E.F.Forward and Spot Exchange Rates.” J. Monetary Econ. 14(1984):319-38.CrossRefGoogle Scholar
Figlewski, S., and Urich, T.. “Optimal Aggregation of Money Supply Forecasts: Accuracy, Profitability, and Market Efficiency.” J. Finance 38(1983):695710.CrossRefGoogle Scholar
Freund, R.J.The Introduction of Risk into a Programming Model.” Econometrica 24(1956):253-63.CrossRefGoogle Scholar
Hein, S.E., and Spudeck, R.E.. “Forecasting the Daily Federal Funds Rate.” Internat. J. Forecasting 4(1988):581-91.CrossRefGoogle Scholar
Leitch, G., and Tanner, J.E.. “Economic Forecast Evaluation: Profits versus the Conventional Error Measures.” Amer. Econ. Rev. 81(1991):580-90.Google Scholar
Lukac, L.P., Brorsen, B.W., and Irwin, S.H.. “Similarity of Computer Guided Technical Systems.” J. Futures Markets 8(1988a):113.CrossRefGoogle Scholar
Lukac, L.P., Brorsen, B.W., and Irwin, S.H.. “A Test of Futures Market Disequilibrium Using Twelve Different Technical Trading Systems.” Appl. Econ. 20(1988b):623-39.CrossRefGoogle Scholar
Mcintosh, C.S., and Dorfman, J.H.. “A Comparison of Two Performance Measures.” Amer. J. Agr. Econ. 74(1992):209-14.CrossRefGoogle Scholar
Nelson, D.B.Conditional Heteroskedasticity in Asset Returns: A New Approach.” Econometrica 59(1991):347-70.CrossRefGoogle Scholar
Park, T.Forecast Evaluation for Multivariate Time-Series Models: The U.S. Cattle Market.” West. J. Agr. Econ. 15(1990):133-43.Google Scholar
Pratt, J.W.Risk Aversion in the Small and in the Large.” Econometrica 32(1964):122-36.CrossRefGoogle Scholar