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Futures hedging effectiveness under the segmentation of bear/bull energy markets

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  • Chang, Chiao-Yi
  • Lai, Jing-Yi
  • Chuang, I-Yuan
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    Abstract

    This article undertakes eight hedging models (Regression, MD-GARCH, BEKK-GARCH, CCC-GARCH, ECM-MD, ECM-BEKK, ECM-CCC, and state space models) to investigate hedging effectiveness of different price scenarios in energy futures markets. Different models have systematically evidenced that hedging effectiveness is higher in an increasing pattern (termed "bull markets") than in a decreasing pattern (termed "bear markets") for crude oil and gasoline futures. That is, findings show asymmetric hedging performance between upward and downward price trends consistently from the most popular hedging models in literature. Out-of-sample examination also suggests that the ranking of hedging effectiveness of different hedging models is not parallel in different price patterns across futures contracts, implying that investors should adjust their hedging strategies accordingly.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 32 (2010)
    Issue (Month): 2 (March)
    Pages: 442-449

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    Handle: RePEc:eee:eneeco:v:32:y:2010:i:2:p:442-449

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

    Related research

    Keywords: Hedge ratio Energy futures GARCH model;

    References

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    1. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543.
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    3. 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(04), pages 535-551, December.
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    Citations

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    Cited by:
    1. Krzysztof Osiewalski & Jacek Osiewalski, 2013. "A Long-Run Relationship between Daily Prices on Two Markets: The Bayesian VAR(2)–MSF-SBEKK Model," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 5(1), pages 65-83, March.
    2. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    3. Sadorsky, Perry, 2012. "Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies," Energy Economics, Elsevier, vol. 34(1), pages 248-255.
    4. Chang, Kuang-Liang, 2012. "Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market," Energy Economics, Elsevier, vol. 34(1), pages 294-306.

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