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A bivariate Markov regime switching GARCH approach to estimate time varying minimum variance hedge ratios

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  • Hsiang-Tai Lee
  • Jonathan Yoder

Abstract

This article develops a new bivariate Markov regime switching BEKK-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) (RS-BEKK-GARCH) model. The model is a state-dependent bivariate BEKK-GARCH model and an extension of Gray's univariate generalized regime-switching (GRS) model to the bivariate case. To solve the path-dependency problem inherent in the bivariate regime switching BEKK-GARCH model, we propose a recombining method for the covariance term in the conditional variance-covariance matrix. The model is applied to estimate time-varying minimum variance hedge ratios for corn and nickel spot and futures prices. Out-of-sample point estimates of hedging portfolio variance show that compared to the state-independent BEKK-GARCH model, the RS-BEKK-GARCH model improves out-of-sample hedging effectiveness for both corn and nickel data. We perform White's (2000) data-snooping reality check to test for predictive superiority of RS-BEKK-GARCH over the benchmark model and find that the difference in variance reduction between BEKK-GARCH and RS-BEKK-GARCH is not statistically significant for either data set at conventional confidence levels.

Suggested Citation

  • Hsiang-Tai Lee & Jonathan Yoder, 2007. "A bivariate Markov regime switching GARCH approach to estimate time varying minimum variance hedge ratios," Applied Economics, Taylor & Francis Journals, vol. 39(10), pages 1253-1265.
    • Handle: RePEc:taf:applec:v:39:y:2007:i:10:p:1253-1265
    DOI: 10.1080/00036840500438970
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    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. 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 109-124, April-Jun.
    3. Ederington, Louis H, 1979. "The Hedging Performance of the New Futures Markets," Journal of Finance, American Finance Association, vol. 34(1), pages 157-170, March.
    4. Gannon, Gerard & Au-Yeung, Siu Pang, 2004. "Structural effects and spillovers in HSIF, HSI and S&P500 volatility," Working Papers aef_2004_08, Deakin University, Department of Economics.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. H. N. E. BystrOm, 2003. "The hedging performance of electricity futures on the Nordic power exchange," Applied Economics, Taylor & Francis Journals, vol. 35(1), pages 1-11.
    7. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    8. Gannon, Gerard & Au-Yeung, Siu Pang, 2004. "Structural effects and spillovers in HSIF, HSI and S&P500 volatility," Research in International Business and Finance, Elsevier, vol. 18(3), pages 305-317, September.
    9. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    10. Chris Brooks & Olan T. Henry & Gita Persand, 2002. "The Effect of Asymmetries on Optimal Hedge Ratios," The Journal of Business, University of Chicago Press, vol. 75(2), pages 333-352, April.
    11. 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(4), pages 535-551, December.
    12. Tae H. Park & Lorne N. Switzer, 1995. "Bivariate GARCH estimation of the optimal hedge ratios for stock index futures: A note," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(1), pages 61-67, February.
    13. Anil K. Bera & Philip Garcia & Jae-Sun Roh, 1997. "Estimation of Time-Varying Hedge Ratios for Corn and Soybeans: BGARCH and Random Coefficient Approaches," Finance 9712007, University Library of Munich, Germany.
    14. repec:cup:etheor:v:11:y:1995:i:1:p:122-50 is not listed on IDEAS
    15. Alizadeh, Amir H. & Nomikos, Nikos K. & Pouliasis, Panos K., 2008. "A Markov regime switching approach for hedging energy commodities," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1970-1983, September.
    16. Gagnon, Louis & Lypny, Gregory J. & McCurdy, Thomas H., 1998. "Hedging foreign currency portfolios," Journal of Empirical Finance, Elsevier, vol. 5(3), pages 197-220, September.
    17. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    18. Figlewski, Stephen, 1984. "Hedging Performance and Basis Risk in Stock Index Futures," Journal of Finance, American Finance Association, vol. 39(3), pages 657-669, July.
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    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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