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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

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.

Suggested Citation

  • Hou, Yang & Li, Steven, 2013. "Hedging performance of Chinese stock index futures: An empirical analysis using wavelet analysis and flexible bivariate GARCH approaches," Pacific-Basin Finance Journal, Elsevier, vol. 24(C), pages 109-131.
  • Handle: RePEc:eee:pacfin:v:24:y:2013:i:c:p:109-131
    DOI: 10.1016/j.pacfin.2013.04.001
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    Cited by:

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    4. Huang, Ying Sophie & Yao, Juan & Zhu, Yu, 2018. "Thriving in a disrupted market: a study of Chinese hedge fund performance," Pacific-Basin Finance Journal, Elsevier, vol. 48(C), pages 210-223.
    5. Qu, Hui & Wang, Tianyang & Zhang, Yi & Sun, Pengfei, 2019. "Dynamic hedging using the realized minimum-variance hedge ratio approach – Examination of the CSI 300 index futures," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    6. Wu, Lei & Zeng, Hongchao, 2019. "The impact of liquidity constraints on the cash-futures basis dynamics: Evidence from the Chinese market," Economic Modelling, Elsevier, vol. 83(C), pages 96-110.
    7. Mandeep Kaur & Kapil Gupta, 2019. "Estimating Hedging Effectiveness Using Variance Reduction And Risk-Return Approaches: Evidence From National Stock Exchange Of India," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 8(4), pages 149-169.
    8. Hou, Yang & Holmes, Mark, 2017. "On the effects of static and autoregressive conditional higher order moments on dynamic optimal hedging," MPRA Paper 82000, University Library of Munich, Germany.

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    More about this item

    Keywords

    Hedge ratio; Hedging effectiveness; Wavelet analysis; Bivariate GARCH;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G19 - Financial Economics - - General Financial Markets - - - Other
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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