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A Bivariate Markov Regime Switching GARCH Approach to Estimate Time Varying Minimum Variance Hedge Ratios

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Author Info

  • Hsiang-Tai Lee

    (Washington State University)

  • Jonathan Yoder

    (Washington State University)

Abstract

This paper develops a new bivariate Markov regime switching BEKK-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.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0506009.

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Length: 33 pages
Date of creation: 28 Jun 2005
Date of revision:
Handle: RePEc:wpa:wuwpem:0506009

Note: Type of Document - pdf; pages: 33
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Web page: http://128.118.178.162

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Keywords: bivariate GARCH; require switching; hedging;

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References

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  1. 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.
  2. 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.
  3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  4. Figlewski, Stephen, 1984. " Hedging Performance and Basis Risk in Stock Index Futures," Journal of Finance, American Finance Association, vol. 39(3), pages 657-69, July.
  5. 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.
  6. Ederington, Louis H, 1979. "The Hedging Performance of the New Futures Markets," Journal of Finance, American Finance Association, vol. 34(1), pages 157-70, March.
  7. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
  8. repec:cup:etheor:v:11:y:1995:i:1:p:122-50 is not listed on IDEAS
  9. 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.
  10. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
  11. 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.
  12. 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.
  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, EconWPA.
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Cited by:
  1. Zhu, Hui-Ming & Li, Rong & Li, Sufang, 2014. "Modelling dynamic dependence between crude oil prices and Asia-Pacific stock market returns," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 208-223.
  2. 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.
  3. Wagner Oliveira Monteiro & Rodrigo De Losso da Silveira Bueno, 2011. "Dynamic Hedging inMarkov Regimes Switching," Anais do XXXVII Encontro Nacional de Economia [Proceedings of the 37th Brazilian Economics Meeting] 136, ANPEC - Associação Nacional dos Centros de Pósgraduação em Economia [Brazilian Association of Graduate Programs in Economics].
  4. Rozaimah Zainudin & Roselee Shah Shaharudin, 2011. "Multi Mean Garch Approach to Evaluating Hedging Performance in the Crude Palm Oil Futures Market," Asian Academy of Management Journal of Accounting and Finance, Penerbit Universiti Sains Malaysia, vol. 7(1), pages 111-130.
  5. Lee, Hsiang-Tai, 2010. "Regime switching correlation hedging," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2728-2741, November.
  6. Dinica, Mihai Cristian & Armeanu, Daniel, 2014. "The Optimal Hedging Ratio for Non-Ferrous Metals," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 105-122, March.

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