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Hedging effectiveness under conditions of asymmetry

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  • John Cotter
  • Jim Hanly

Abstract

We examine whether hedging effectiveness is affected by asymmetry in the return distribution by applying tail-specific metrics, for example, value at risk, to compare the hedging effectiveness of short and long hedgers. Comparisons are applied to a number of hedging strategies including OLS and both symmetric and asymmetric generalised autoregressive conditional heteroskedastic models. We apply our analysis to a dataset consisting of S&P500 index cash and futures containing symmetric and asymmetric return distributions chosen ex post . Our findings show that asymmetry reduces out-of-sample hedging performance and that significant differences occur in hedging performance between short and long hedgers.

Suggested Citation

  • John Cotter & Jim Hanly, 2012. "Hedging effectiveness under conditions of asymmetry," The European Journal of Finance, Taylor & Francis Journals, vol. 18(2), pages 135-147, February.
  • Handle: RePEc:taf:eurjfi:v:18:y:2012:i:2:p:135-147
    DOI: 10.1080/1351847X.2011.574977
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    References listed on IDEAS

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    Cited by:

    1. Bessler, Wolfgang & Leonhardt, Alexander & Wolff, Dominik, 2016. "Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 239-256.
    2. Bessler, Wolfgang & Wolff, Dominik, 2014. "Hedging European government bond portfolios during the recent sovereign debt crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 33(C), pages 379-399.
    3. Pan, Zhiyuan & Wang, Yudong & Yang, Li, 2014. "Hedging crude oil using refined product: A regime switching asymmetric DCC approach," Energy Economics, Elsevier, vol. 46(C), pages 472-484.
    4. Chuang, Chung-Chu & Wang, Yi-Hsien & Yeh, Tsai-Jung & Chuang, Shuo-Li, 2014. "Backtesting VaR in consideration of the higher moments of the distribution for minimum-variance hedging portfolios," Economic Modelling, Elsevier, vol. 42(C), pages 15-19.
    5. Jing-Yi Lai, 2012. "An empirical study of the impact of skewness and kurtosis on hedging decisions," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1827-1837, December.

    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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