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Hedging China’s Energy Oil Market Risks

Author

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  • Su, Yongyang
  • Lau, Chi Keung Marco
  • Tan, Na

Abstract

This paper is the first study to examine the effectiveness of the Shanghai Fuel Oil Futures Contract (SHF) in risk reduction on the Chinese energy oil market. We find that the SHF contract can help investors reduce risk by approximately 45%, lower than empirical evidence in developed markets, when weekly data are applied. In contrast, when using daily data SHF contract can only help reduce risk by approximately 9%. The Tokyo Oil Futures Contract (TKF), however, performs two times better, reducing risk by around 17%. The empirical results are robust when variance complicated bivariate GARCH (BGARCH) and bivariate distributions are used. Our results imply the energy oil futures market in China is not well-established and further policy is needed to improve market efficiency.

Suggested Citation

  • Su, Yongyang & Lau, Chi Keung Marco & Tan, Na, 2013. "Hedging China’s Energy Oil Market Risks," MPRA Paper 47134, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:47134
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    File URL: https://mpra.ub.uni-muenchen.de/47134/1/MPRA_paper_47134.pdf
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    References listed on IDEAS

    as
    1. Sung Yong Park & Sang Young Jei, 2010. "Estimation and hedging effectiveness of time‐varying hedge ratio: Flexible bivariate garch approaches," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(1), pages 71-99, January.
    2. 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.
    3. West, Kenneth D. & Cho, Dongchul, 1995. "The predictive ability of several models of exchange rate volatility," Journal of Econometrics, Elsevier, vol. 69(2), pages 367-391, October.
    4. Lien, Donald, 2009. "A note on the hedging effectiveness of GARCH models," International Review of Economics & Finance, Elsevier, vol. 18(1), pages 110-112, January.
    5. 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.
    6. 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.
    7. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-362, July.
    8. Atreya Chakraborty & John Barkoulas, 1999. "Dynamic futures hedging in currency markets," The European Journal of Finance, Taylor & Francis Journals, vol. 5(4), pages 299-314.
    9. 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 339-350, July.
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    Cited by:

    1. Martínez Ceseña, Eduardo A. & Good, Nicholas & Mancarella, Pierluigi, 2015. "Electrical network capacity support from demand side response: Techno-economic assessment of potential business cases for small commercial and residential end-users," Energy Policy, Elsevier, vol. 82(C), pages 222-232.

    More about this item

    Keywords

    China Energy Oil Market; Hedging Risk Performance; Bivariate GARCH model.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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