Sankhya: The Indian Journal of Statistics

1997, Volume 59, Series B, Pt. 3, 346-368

ESTIMATION OF TIME-VARYING HEDGE RATIOS FOR CORN AND SOYBEANS: BGARCH AND RANDOM COEFFICIENT APPROACHES

By

ANIL K. BERA, PHILIP GARCIA, University of Illinois at Urbana-Champaign

And

JAE-SUN ROH, Seoul National University, Seoul

SUMMARY. 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 BG ARCH model provides the largest reduction in the variance of the return portfolio.

AMS (1980) subject classification. 62M10, 62P20

Key words and phrases. Hedge ratio, ARCH model, random coefficient model, model testing, model selection

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