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A utility based approach to energy hedging

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  • Cotter, John
  • Hanly, Jim

Abstract

A key issue in the estimation of energy hedges is the hedgers' attitude towards risk which is encapsulated in the form of the hedgers' utility function. However, the literature typically uses only one form of utility function such as the quadratic when estimating hedges. This paper addresses this issue by estimating and applying energy market based risk aversion to commonly applied utility functions including log, exponential and quadratic, and we incorporate these in our hedging frameworks. We find significant differences in the optimal hedge strategies based on the utility function chosen.

Suggested Citation

  • Cotter, John & Hanly, Jim, 2012. "A utility based approach to energy hedging," Energy Economics, Elsevier, vol. 34(3), pages 817-827.
    • Handle: RePEc:eee:eneeco:v:34:y:2012:i:3:p:817-827
    DOI: 10.1016/j.eneco.2011.07.009
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    Cited by:

    1. Furió, Dolores & Torró, Hipòlit, 2020. "Optimal hedging under biased energy futures markets," Energy Economics, Elsevier, vol. 88(C).
    2. Jim Hanly, 2017. "Managing Energy Price Risk using Futures Contracts: A Comparative Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    3. Conlon, Thomas & Cotter, John, 2013. "Downside risk and the energy hedger's horizon," Energy Economics, Elsevier, vol. 36(C), pages 371-379.
    4. Cotter, John & Hanly, Jim, 2015. "Performance of utility based hedges," Energy Economics, Elsevier, vol. 49(C), pages 718-726.
    5. Dinica, Mihai Cristian & Armeanu, Daniel, 2014. "The Optimal Hedging Ratio for Non-Ferrous Metals," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 105-122, March.
    6. Shrestha, Keshab & Subramaniam, Ravichandran & Peranginangin, Yessy & Philip, Sheena Sara Suresh, 2018. "Quantile hedge ratio for energy markets," Energy Economics, Elsevier, vol. 71(C), pages 253-272.
    7. Shrestha, Keshab & Subramaniam, Ravichandran & Rassiah, Puspavathy, 2017. "Pure martingale and joint normality tests for energy futures contracts," Energy Economics, Elsevier, vol. 63(C), pages 174-184.
    8. Barbi, Massimiliano & Romagnoli, Silvia, 2018. "Skewness, basis risk, and optimal futures demand," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 14-29.
    9. Cui, Yan & Feng, Yun, 2020. "Composite hedge and utility maximization for optimal futures hedging," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 15-32.
    10. Arunanondchai, Panit & Sukcharoen, Kunlapath & Leatham, David J., 2020. "Dealing with tail risk in energy commodity markets: Futures contracts versus exchange-traded funds," Journal of Commodity Markets, Elsevier, vol. 20(C).
    11. Charalampous, Georgios & Madlener, Reinhard, 2013. "Risk Management and Portfolio Optimization for Gas- and Coal-fired Power Plants in Germany: A Multivariate GARCH Approach," FCN Working Papers 23/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    12. Panos K. Pouliasis & Ilias D. Visvikis & Nikos C. Papapostolou & Alexander A. Kryukov, 2020. "A novel risk management framework for natural gas markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(3), pages 430-459, March.
    13. Chiou-Wei, Song-Zan & Chen, Sheng-Hung & Zhu, Zhen, 2020. "Natural gas price, market fundamentals and hedging effectiveness," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 321-337.
    14. Devine, Mel & Farrell, Niall & Lee, William, 2014. "Managing investor and consumer exposure to electricity market price risks through Feed-in Tariff design," MPRA Paper 59208, University Library of Munich, Germany.
    15. Chai, Shanglei & Zhou, P., 2018. "The Minimum-CVaR strategy with semi-parametric estimation in carbon market hedging problems," Energy Economics, Elsevier, vol. 76(C), pages 64-75.

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    More about this item

    Keywords

    Energy; Hedging; Risk management; Risk aversion; Forecasting;
    All these keywords.

    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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