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

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Author Info

  • Anil K. Bera

    (University of Illinois at Urbana-Champaign)

  • Philip Garcia

    (University of Illinois at Urbana-Champaign)

  • Jae-Sun Roh

    (Seoul National University)

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.

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Bibliographic Info

Paper provided by EconWPA in its series Finance with number 9712007.

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Length: 35 pages
Date of creation: 18 Dec 1997
Date of revision:
Handle: RePEc:wpa:wuwpfi:9712007

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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Related research

Keywords: Optimal Hedge Ratios; Conditional Heteroskedasticity; BGARCH;

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References

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  1. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  2. Robert B. Davies, 2002. "Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case," Biometrika, Biometrika Trust, vol. 89(2), pages 484-489, June.
  3. Anderson, Ronald W & Danthine, Jean-Pierre, 1980. " Hedging and Joint Production: Theory and Illustrations," Journal of Finance, American Finance Association, vol. 35(2), pages 487-98, May.
  4. Pagan, Adrian, 1980. "Some identification and estimation results for regression models with stochastically varying coefficients," Journal of Econometrics, Elsevier, vol. 13(3), pages 341-363, August.
  5. Baillie, Richard T. & Bollerslev, Tim, 1990. "A multivariate generalized ARCH approach to modeling risk premia in forward foreign exchange rate markets," Journal of International Money and Finance, Elsevier, vol. 9(3), pages 309-324, September.
  6. Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(04), pages 535-551, December.
  7. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
  8. repec:cup:etheor:v:11:y:1995:i:1:p:122-50 is not listed on IDEAS
  9. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-24, April-Jun.
  10. Bera, A.K. & Higgins, M.L., 1990. "A Test For Conditional Heterskedasticity In Time Series Midels," University of Western Ontario, The Centre for the Study of International Economic Relations Working Papers 9003, University of Western Ontario, The Centre for the Study of International Economic Relations.
  11. Tong, Wilson H. S., 1996. "An examination of dynamic hedging," Journal of International Money and Finance, Elsevier, vol. 15(1), pages 19-35, February.
  12. Bos, T & Newbold, P, 1984. "An Empirical Investigation of the Possibility of Stochastic Systematic Risk in the Market Model," The Journal of Business, University of Chicago Press, vol. 57(1), pages 35-41, January.
  13. E.K. Berndt & B.H. Hall & R.E. Hall, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 103-116 National Bureau of Economic Research, Inc.
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