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Learning sequential option hedging models from market data

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  • Nian, Ke
  • Coleman, Thomas F
  • Li, Yuying

Abstract

Following a direct data-driven approach, we propose a robust encoder-decoder Gated Recurrent Unit (GRU), GRUδ, for optimal discrete option hedging. The proposed GRUδ utilizes the Black-Scholes model as a pre-trained model and incorporates sequential information and feature selection. Using the S&P 500 index European option market data, we demonstrate that the weekly and monthly hedging performance of the proposed GRUδ significantly surpasses that of the data-driven minimum variance (MV) method, the regularized kernel data-driven model, and the SABR-Bartlett method. In addition, the daily hedging performance of the proposed GRUδ also surpasses that of MV methods based on parametric models, the kernel method, and SABR-Bartlett method.

Suggested Citation

  • Nian, Ke & Coleman, Thomas F & Li, Yuying, 2021. "Learning sequential option hedging models from market data," Journal of Banking & Finance, Elsevier, vol. 133(C).
    • Handle: RePEc:eee:jbfina:v:133:y:2021:i:c:s0378426621002338
    DOI: 10.1016/j.jbankfin.2021.106277
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    References listed on IDEAS

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    1. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    2. Hans Buehler & Lukas Gonon & Josef Teichmann & Ben Wood & Baranidharan Mohan & Jonathan Kochems, 2019. "Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning," Swiss Finance Institute Research Paper Series 19-80, Swiss Finance Institute.
    3. Ke Nian & Thomas F. Coleman & Yuying Li, 2018. "Learning minimum variance discrete hedging directly from the market," Quantitative Finance, Taylor & Francis Journals, vol. 18(7), pages 1115-1128, July.
    4. Ramazan Gencay & Aslihan Salih, 2003. "Degree of Mispricing with the Black-Scholes Model and Nonparametric Cures," Annals of Economics and Finance, Society for AEF, vol. 4(1), pages 73-101, May.
    5. Nathan Lassance & Frédéric Vrins, 2018. "A comparison of pricing and hedging performances of equity derivatives models," Applied Economics, Taylor & Francis Journals, vol. 50(10), pages 1122-1137, February.
    6. S. G. Kou, 2002. "A Jump-Diffusion Model for Option Pricing," Management Science, INFORMS, vol. 48(8), pages 1086-1101, August.
    7. repec:dau:papers:123456789/11532 is not listed on IDEAS
    8. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    9. Tim Bollerslev & Julia Litvinova & George Tauchen, 2006. "Leverage and Volatility Feedback Effects in High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 353-384.
    10. Heston, Steven L & Nandi, Saikat, 2000. "A Closed-Form GARCH Option Valuation Model," Review of Financial Studies, Society for Financial Studies, vol. 13(3), pages 585-625.
    11. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    12. Bernard Dumas & Jeff Fleming & Robert E. Whaley, 1998. "Implied Volatility Functions: Empirical Tests," Journal of Finance, American Finance Association, vol. 53(6), pages 2059-2106, December.
    13. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    14. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    15. C. He & J. Kennedy & T. Coleman & P. Forsyth & Y. Li & K. Vetzal, 2006. "Calibration and hedging under jump diffusion," Review of Derivatives Research, Springer, vol. 9(1), pages 1-35, January.
    16. Hull, John & White, Alan, 2017. "Optimal delta hedging for options," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 180-190.
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    Cited by:

    1. Chuting Sun & Qi Wu & Xing Yan, 2023. "Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning," Papers 2301.07318, arXiv.org, revised Jan 2024.

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    More about this item

    Keywords

    Option; Discrete hedging; Data-Driven model; Feature selection; Feature extraction; Machine learning; Recurrent neural network;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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