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Commodity Price Behavior: A Rational Expectations Storage Model of Corn

Listed author(s):
  • Peterson, Hikaru Hanawa
  • Tomek, William G.

A structural model is developed to simulate the probability distributions of corn prices by month. The intent is to determine the relationship between model specifications, based on a rational expectations competitive storage framework, and the probability distributions of monthly prices. Specifically, can a structural model generate corn prices with characteristics that are consistent with those observed in the 1990s? The model in this paper produces cash prices that inter alia have positively skewed distributions where the mean and variance increase over the storage season. The model also generates futures prices as conditional expectations of spot prices at contract maturity. The variances of these futures prices have realistic time-to-maturity and seasonal effects. The model is solved and simulated so that the consequences of making the model increasingly complex can be determined. A “curse of dimensionality” is inevitable with the increased complexity, resulting in lengthy computing times, but the final specification generates plausible probability distributions. In contrast to other models in the literature, our specification does not depend on the unrealistic assumption of zero stock-levels to generate skewed price distributions and the Abackwardation@ commonly observed in prices between crop years. Non-linearity in the supply of storage is achieved by modeling convenience yield. The model can be used to depict price behavior conditional on varying levels of the state variables, e.g., for large or small stock levels. Having created realistic probability distributions of prices, a logical next step is to use the distributions to appraise marketing strategies to manage price risk for corn.

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File URL: http://purl.umn.edu/127682
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Paper provided by Cornell University, Department of Applied Economics and Management in its series Working Papers with number 127682.

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Date of creation: Dec 2000
Handle: RePEc:ags:cudawp:127682
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  1. Scott H. Irwin & Carl R. Zulauf & Thomas E. Jackson, 1996. "Monte Carlo Analysis of Mean Reversion in Commodity Futures Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 387-399.
  2. Michaelides, Alexander & Ng, Serena, 2000. "Estimating the rational expectations model of speculative storage: A Monte Carlo comparison of three simulation estimators," Journal of Econometrics, Elsevier, vol. 96(2), pages 231-266, June.
  3. Bruce L. Gardner & Ramón López, 1996. "The Inefficiency of Interest-Rate Subsidies in Commodity Price Stabilization," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(3), pages 508-516.
  4. Darren L. Frechette & Paul L. Fackler, 1999. "What Causes Commodity Price Backwardation?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 761-771.
  5. Miranda, Mario J & Glauber, Joseph W, 1993. "Estimation of Dynamic Nonlinear Rational Expectations Models of Primary Commodity Markets with Private and Government Stockholding," The Review of Economics and Statistics, MIT Press, vol. 75(3), pages 463-470, August.
  6. Holt, Matthew T., 1999. "A Linear Approximate Acreage Allocation Model," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 24(02), December.
  7. Goodwin, Barry K. & Roberts, Matthew C. & Coble, Keith H., 2000. "Measurement Of Price Risk In Revenue Insurance: Implications Of Distributional Assumptions," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(01), July.
  8. Sergio H. Lence & Dermot J. Hayes, 2002. "U.S. Farm Policy and the Volatility of Commodity Prices and Farm Revenues," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(2), pages 335-351.
  9. Williams,Jeffrey C. & Wright,Brian D., 2005. "Storage and Commodity Markets," Cambridge Books, Cambridge University Press, number 9780521023399, September.
  10. Miranda, Mario J, 1998. "Numerical Strategies for Solving the Nonlinear Rational Expectations Commodity Market Model," Computational Economics, Springer;Society for Computational Economics, vol. 11(1-2), pages 71-87, April.
  11. Brorsen, B. Wade & Irwin, Scott H., 1996. "Improving The Relevance Of Research On Price Forecasting And Marketing Strategies," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 25(1), April.
  12. Garcia, Philip & Irwin, Scott H. & Leuthold, Raymond M. & Yang, Li, 1997. "The value of public information in commodity futures markets," Journal of Economic Behavior & Organization, Elsevier, vol. 32(4), pages 559-570, April.
  13. Robert N. Wisner & E. Neal Blue & E. Dean Baldwin, 1998. "Preharvest Marketing Strategies Increase Net Returns for Corn and Soybean Growers," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 20(2), pages 288-307.
  14. Seung-Ryong Yang & B. Wade Brorsen, 1992. "Nonlinear Dynamics of Daily Cash Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 74(3), pages 706-715.
  15. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
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