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Impacts of Permanent and Transitory Shocks on Optimal Length of Moving Average to Predict Wheat Basis

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  • Lee, Yoonsuk
  • Brorsen, B. Wade

Abstract

A new stochastic process is introduced where permanent changes occur following a Poisson jump process and temporary changes occur following a normal distribution. The model is estimated using hard wheat basis data and is used to explain why the optimal length of moving average to forecast basis varies over time. The estimated probability of jumps is large and thus the optimal length of moving average is small.

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File URL: http://purl.umn.edu/125001
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Bibliographic Info

Paper provided by Agricultural and Applied Economics Association in its series 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington with number 125001.

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Date of creation: 2012
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Handle: RePEc:ags:aaea12:125001

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Keywords: basis; jump-diffusion process; Monte Carlo simulation; Research and Development/Tech Change/Emerging Technologies; Research Methods/ Statistical Methods;

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  1. Hansen, Bruce E & West, Kenneth D, 2002. "Generalized Method of Moments and Macroeconomics," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 460-69, October.
  2. Akgiray, Vedat & Booth, G Geoffrey, 1988. "Mixed Diffusion-Jump Process Modeling of Exchange Rate Movements," The Review of Economics and Statistics, MIT Press, vol. 70(4), pages 631-37, November.
  3. Hatchett, Robert B. & Brorsen, B. Wade & Anderson, Kim B., 2009. "Optimal Length of Moving Average to Forecast Futures Basis," 2009 Conference, April 20-21, 2009, St. Louis, Missouri 53048, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  4. Mankiw, N. Gregory & Campbell, John, 1987. "Permanent and Transitory Components in Macroeconomic Fluctuations," Scholarly Articles 3207697, Harvard University Department of Economics.
  5. Steen Koekebakker & Gudbrand Lien, 2004. "Volatility and Price Jumps in Agricultural Futures Prices—Evidence from Wheat Options," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1018-1031.
  6. Junsoo Lee & Mark C. Strazicich, 2003. "Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1082-1089, November.
  7. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  8. Sanders, Dwight R. & Manfredo, Mark R., 2006. "Forecasting Basis Levels in the Soybean Complex: A Comparison of Time Series Methods," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(03), December.
  9. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," CESifo Working Paper Series 1237, CESifo Group Munich.
  10. Tauchen, George E. & Gallant, A. Ronald, 1995. "Which Moments to Match," Working Papers 95-20, Duke University, Department of Economics.
  11. Dykema, Amy & Klein, Nicole L. & Taylor, Gary, 2002. "The Widening Corn Basis In South Dakota: Factors Affecting And The Impact Of The Loan Deficiency Payment," 2002 Annual Meeting, July 28-31, 2002, Long Beach, California 36574, Western Agricultural Economics Association.
  12. Hilliard, Jimmy E. & Reis, Jorge, 1998. "Valuation of Commodity Futures and Options under Stochastic Convenience Yields, Interest Rates, and Jump Diffusions in the Spot," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(01), pages 61-86, March.
  13. Hall, Joyce A. & Brorsen, B. Wade & Irwin, Scott H., 1989. "The Distribution of Futures Prices: A Test of the Stable Paretian and Mixture of Normals Hypotheses," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 24(01), pages 105-116, March.
  14. Merton, Robert C., 1975. "Option pricing when underlying stock returns are discontinuous," Working papers 787-75., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  15. Andersen, Torben G. & Chung, Hyung-Jin & Sorensen, Bent E., 1999. "Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study," Journal of Econometrics, Elsevier, vol. 91(1), pages 61-87, July.
  16. Hayenga, Marvin L. & Jiang, Bingrong, 1997. "Corn and Soybean Basis Behavior and Forecasting: Fundamental and Alternative Approaches," Staff General Research Papers 10400, Iowa State University, Department of Economics.
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