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Minimizing CVaR in global dynamic hedging with transaction costs

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  • F. Godin

Abstract

This study develops a global derivatives hedging methodology which takes into account the presence of transaction costs. It extends the Hodges and Neuberger [ Rev. Futures Markets , 1989, 8 , 222--239] framework in two ways. First, to reduce the occurrence of extreme losses, the expected utility is replaced by the conditional Value-at-Risk (CVaR) coherent risk measure as the objective function. Second, the normality assumption for the underlying asset returns is relaxed: general distributions are considered to improve the realism of the model and to be consistent with fat tails observed empirically. Dynamic programming is used to solve the hedging problem. The CVaR minimization objective is shown to be part of a time-consistent framework. Simulations with parameters estimated from the S&P 500 financial time series show the superiority of the proposed hedging method over multiple benchmarks from the literature in terms of tail risk reduction.

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  • F. Godin, 2016. "Minimizing CVaR in global dynamic hedging with transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 461-475, March.
    • Handle: RePEc:taf:quantf:v:16:y:2016:i:3:p:461-475
    DOI: 10.1080/14697688.2015.1054865
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    Cited by:

    1. Karim Barigou & Valeria Bignozzi & Andreas Tsanakas, 2021. "Insurance valuation: A two-step generalised regression approach," Post-Print hal-03043244, HAL.
    2. Alexandre Carbonneau & Fr'ed'eric Godin, 2020. "Equal Risk Pricing of Derivatives with Deep Hedging," Papers 2002.08492, arXiv.org, revised Jun 2020.
    3. Karim Barigou & Valeria Bignozzi & Andreas Tsanakas, 2020. "Insurance valuation: A two-step generalised regression approach," Papers 2012.04364, arXiv.org, revised Nov 2021.
    4. Jitmaneeroj, Boonlert, 2018. "The effect of the rebalancing horizon on the tradeoff between hedging effectiveness and transaction costs," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 282-298.
    5. Maciej Augustyniak & Frédéric Godin & Clarence Simard, 2017. "Assessing the effectiveness of local and global quadratic hedging under GARCH models," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1305-1318, September.
    6. Pascal Franc{c}ois & Genevi`eve Gauthier & Fr'ed'eric Godin & Carlos Octavio P'erez Mendoza, 2024. "Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information," Papers 2407.21138, arXiv.org.
    7. Michèle Breton & Frédéric Godin, 2017. "Global Hedging through Post-Decision State Variables," JRFM, MDPI, vol. 10(3), pages 1-6, August.
    8. Joakim Dimoski & Stein-Erik Fleten & Nils Löhndorf & Sveinung Nersten, 2023. "Dynamic hedging for the real option management of hydropower production with exchange rate risks," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(2), pages 525-554, June.
    9. Marcelo Righi, 2024. "Optimal hedging with variational preferences under convex risk measures," Papers 2407.03431, arXiv.org, revised Oct 2024.
    10. Carbonneau, Alexandre, 2021. "Deep hedging of long-term financial derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 327-340.
    11. Alexandre Carbonneau, 2020. "Deep Hedging of Long-Term Financial Derivatives," Papers 2007.15128, arXiv.org.
    12. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures," Papers 2107.11340, arXiv.org.
    13. Strub, Moris S. & Li, Duan & Cui, Xiangyu & Gao, Jianjun, 2019. "Discrete-time mean-CVaR portfolio selection and time-consistency induced term structure of the CVaR," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    14. Karim Barigou & Valeria Bignozzi & Andreas Tsanakas, 2021. "Insurance valuation: A two-step generalised regression approach," Working Papers hal-03043244, HAL.
    15. Parisa Davar & Fr'ed'eric Godin & Jose Garrido, 2024. "Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients," Papers 2406.15612, arXiv.org, revised Jun 2024.

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