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Bayesian Cross Hedging: An Example from the Soybean Market
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F. Douglas Foster and Charles H. Whiteman
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Abstract
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Following Lence and Hayes (1994a), we study the problem faced by an
Iowa farmer who wishes to hedge a soybean harvest using Chicago futures
contracts. A time-series model for spot and futures prices is postulated,
and numerical Bayesian procedures are used to calculate predictive
densities and optimal hedges. The numerical procedures extend earlier
analytical work, and easily accommodate alternative views about
specification (levels vs. logarithms, trends vs. no trends, etc.),
uncertainty about parameterisations (estimation risk), as well as
other non-sample information (the likely size of the difference between
spot prices in Iowa and Chicago, the tendency of the basis to be large
in the spring, the shrinking of the basis as expiration of the future
looms, etc.).
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Keywords
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BAYESIAN DECISION MAKING; ESTIMATION RISK; PREDICTIVE DISTRIBUTION;
INFORMATIVE PRIOR; IMPORTANCE SAMPLING.
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Contact Details
F. Douglas Foster
Australian Graduate School of Management
The University of New South Wales
Sydney NSW 2052
E-mail: fd.foster@unsw.edu.au
Charles H. Whiteman
Department of Economics
The University of Iowa
Iowa City IA 52242
USA
E-mail: whiteman@uiowa.edu
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We thank Dermot Hayes and Sergio Lence for helpful discussions and
data, and the Iowa Department of Agriculture for daily historical
data on soybean prices. Discussions with Ravi Jagannathan,
Narayana Kocherlakota, Alejandro Manelli, Tom Smith and Robert
Whaley were helpful. In addition, comments by seminar participants
at the Australian Graduate School of Management, Boston College,
Indiana University, Iowa State University, Pennsylvania State
University, University of Minnesota, University of Nebraska,
University of North Carolina at Chapel Hill, and The University
of Texas at Dallas improved the paper considerably. Foster
gratefully acknowledges the support of the ARC. Whiteman gratefully
acknowledges support of the Institute for Economic Research at
The University of Iowa, as well as support provided for this
research by the NSF under grant SBR 9422873 to the University of Iowa.
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