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Time Varying Risk Aversion: An Application to Energy Hedging

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  • John Cotter
  • Jim Hanly

Abstract

Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive explicit risk aversion based optimal hedge strategies for both short and long hedgers. Out-of-sample results are also presented based on a unique approach that allows us to forecast risk aversion, thereby estimating hedge strategies that address the potential future needs of energy hedgers. We find that the risk aversion based hedges differ significantly from simpler OLS hedges. When implemented in-sample, risk aversion hedges for short hedgers outperform the OLS hedge ratio in a utility based comparison.

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  • John Cotter & Jim Hanly, 2011. "Time Varying Risk Aversion: An Application to Energy Hedging," Papers 1103.5968, arXiv.org.
    • Handle: RePEc:arx:papers:1103.5968
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    Cited by:

    1. Martínez, Beatriz & Torró, Hipòlit, 2015. "European natural gas seasonal effects on futures hedging," Energy Economics, Elsevier, vol. 50(C), pages 154-168.
    2. Martínez, Beatriz & Torró, Hipòlit, 2018. "Hedging spark spread risk with futures," Energy Policy, Elsevier, vol. 113(C), pages 731-746.
    3. Conlon, Thomas & Cotter, John, 2013. "Downside risk and the energy hedger's horizon," Energy Economics, Elsevier, vol. 36(C), pages 371-379.
    4. George E. Halkos & Apostolos S. Tsirivis, 2019. "Energy Commodities: A Review of Optimal Hedging Strategies," Energies, MDPI, vol. 12(20), pages 1-19, October.
    5. Antonio Díaz & Carlos Esparcia, 2021. "Dynamic optimal portfolio choice under time-varying risk aversion," International Economics, CEPII research center, issue 166, pages 1-22.
    6. Cotter, John & Hanly, Jim, 2012. "A utility based approach to energy hedging," Energy Economics, Elsevier, vol. 34(3), pages 817-827.
    7. Gong, Xu & Wen, Fenghua & Xia, X.H. & Huang, Jianbai & Pan, Bin, 2017. "Investigating the risk-return trade-off for crude oil futures using high-frequency data," Applied Energy, Elsevier, vol. 196(C), pages 152-161.
    8. Cotter, John & Hanly, Jim, 2015. "Performance of utility based hedges," Energy Economics, Elsevier, vol. 49(C), pages 718-726.
    9. Demiralay, Sercan & Gencer, Hatice Gaye & Bayraci, Selcuk, 2022. "Carbon credit futures as an emerging asset: Hedging, diversification and downside risks," Energy Economics, Elsevier, vol. 113(C).
    10. Othieno, Ferdinand & Biekpe, Nicholas, 2019. "Estimating the conditional equity risk premium in African frontier markets," Research in International Business and Finance, Elsevier, vol. 47(C), pages 538-551.
    11. Furió, Dolores & Torró, Hipòlit, 2020. "Optimal hedging under biased energy futures markets," Energy Economics, Elsevier, vol. 88(C).

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    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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