Estimation of Time-Varying Hedge Ratios for Corn and Soybeans: BGARCH and Random Coefficient Approaches

Anil Kumar Bera (), Philip Garcia and Jae-Sun Roh
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Philip Garcia: University of Illinois at Urbana-Champaign
Jae-Sun Roh: Seoul National University

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Abstract: This paper deals with the estimation of optimal hedge ratios. A number of recent papers have demonstrated that the ordinary least squares (OLS) method which gives constant hedge ratio is inappropriate and recommended the use of bivariate autoregressive conditional heteroskedastic (BGARCH) model. In this paper we introduce the use of a random coefficient autoregressive (RCAR) model to estimate time varying hedge ratios. Using daily data of spot and futures prices of corn and soybeans we find substantial presence of conditional heteroskedasticity, and also of random coefficients in the regressions of return from the spot market on the return from the futures markets. Hedging performance in terms of variance reduction of returns from alternative models are also conducted. For our data set diagonal vech presentation of BGARCH model provides the largest reduction in the variance of the return portfolio.

Keywords: Optimal Hedge Ratios; Conditional Heteroskedasticity; BGARCH (search for similar items in EconPapers)
JEL-codes: C3 Q Q13 (search for similar items in EconPapers)
Date: 1997-12-18
Note: Type of Document - pdf; prepared on PC; to print on HP Laserjet; pages: 35; figures: included. Office for Futures and Options Research (OFOR) at the University of Illinois at Urbana-Champaign. Working Paper 97-06. For a complete list of OFOR working papers see
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Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpfi:9712007

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