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Hedging performance of Chinese stock index futures: An empirical analysis using wavelet analysis and flexible bivariate GARCH approaches

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  • Hou, Yang
  • Li, Steven
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    Abstract

    In this paper, we assess the hedging performance of the newly established CSI 300 stock index futures over some short hedging horizons. We use wavelet analysis as well as conventional models (naïve, ordinary least squares, and error-correction) to compute the constant hedge ratios. The constant conditional correlation (CCC) and dynamic conditional correlation (DCC) bivariate generalised autoregressive conditional heteroskedasticity (BGARCH) specifications are employed to calculate the time-varying hedge ratios. Overall, we find that the CSI 300 stock index futures can be an effective hedging tool. Among the constant hedge ratio models, the wavelet analysis yields the best in-sample hedging performance, though its out-of-sample hedging performance is similar to other models. Comparing the time-varying ratio models, the CCC BGARCH model is better in terms of in-sample hedging effectiveness while for out-of-sample hedging performance, the DCC model is better with short hedging horizons and CCC model is more favourable with long hedging horizons. Finally, the question whether time-varying ratios outperform constant ratios depends on the length of the hedging horizon. Short horizons favour BGARCH hedging models while long horizons favour constant hedging ratio models.

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    Bibliographic Info

    Article provided by Elsevier in its journal Pacific-Basin Finance Journal.

    Volume (Year): 24 (2013)
    Issue (Month): C ()
    Pages: 109-131

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    Handle: RePEc:eee:pacfin:v:24:y:2013:i:c:p:109-131

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    Web page: http://www.elsevier.com/locate/pacfin

    Related research

    Keywords: Hedge ratio; Hedging effectiveness; Wavelet analysis; Bivariate GARCH;

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    1. Donald Lien & Y. K. Tse & Albert Tsui, 2002. "Evaluating the hedging performance of the constant-correlation GARCH model," Applied Financial Economics, Taylor & Francis Journals, vol. 12(11), pages 791-798.
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    10. 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.
    11. Demirer, Riza & Lien, Donald, 2003. "Downside risk for short and long hedgers," International Review of Economics & Finance, Elsevier, vol. 12(1), pages 25-44.
    12. 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.
    13. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-59, October.
    14. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
    15. 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.
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