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Basis Volatilities of Corn and Soybean in Spatially Separated Markets: The Effect of Ethanol Demand

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  • Bekkerman, Anton
  • Pelletier, Denis

Abstract

The 2006 spike in corn-based ethanol demand has contributed to the increase in basis volatility in corn and soybean markets across the United States, which has, to a significant degree, led to the observed large jumps in the prices of the two commodities. Despite the overall rise in basis volatility, there remain differences in the degree of volatility that exists across spatially separated markets, which might be caused by factors such as transportation costs, seasonality, and time-to-delivery. The focus of this study is threefold first, this work models basis data for six corn and soybean markets by using a multivariate GARCH model that incorporates the spatial linkages of these markets; next, the model is used to investigate whether the increase in ethanol demand has significantly aided in the rise of basis volatilities; and last, the spatio-temporal linkages among basis volatilities in different markets are examined under various scenarios of spot-price shocks.

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Bibliographic Info

Paper provided by Agricultural and Applied Economics Association in its series 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin with number 49281.

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Date of creation: 2009
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Handle: RePEc:ags:aaea09:49281

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Keywords: basis; spatially separated markets; multivariate GARCH; volatility; Agricultural Finance; Demand and Price Analysis; Q11; Q14; G13;

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  1. Crain, Susan J & Lee, Jae Ha, 1996. " Volatility in Wheat Spot and Futures Markets, 1950-1993: Government Farm Programs, Seasonality, and Causality," Journal of Finance, American Finance Association, vol. 51(1), pages 325-43, March.
  2. BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen VK, . "Multivariate GARCH models: a survey," CORE Discussion Papers RP -1847, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Naik, Gopal & Leuthold, Raymond M., 1991. "A Note On The Factors Affecting Corn Basis Relationships," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 23(01), July.
  4. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
  5. Ramirez, Octavio A. & Fadiga, Mohamadou L., 2003. "Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(01), April.
  6. Manfredo, Mark R. & Leuthold, Raymond M. & Irwin, Scott H., 2001. "Forecasting Fed Cattle, Feeder Cattle, And Corn Cash Price Volatility: The Accuracy Of Time Series, Implied Volatility, And Composite Approaches," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 33(03), December.
  7. 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.
  8. 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.
  9. Taylor, Mykel R. & Dhuyvetter, Kevin C. & Kastens, Terry L., 2006. "Forecasting Crop Basis Using Historical Averages Supplemented with Current Market Information," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 31(03), December.
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Cited by:
  1. Busse, Stefan & Brummer, Bernhard & Ihle, Rico, 2010. "Investigating Rapeseed Price Volatilities In The Course Of The Food Crisis," 50st Annual Conference, Braunschweig, Germany, September 29-October 1, 2010 93957, German Association of Agricultural Economists (GEWISOLA).

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