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Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning

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Listed:
  • Jay Cao
  • Jacky Chen
  • Soroush Farghadani
  • John Hull
  • Zissis Poulos
  • Zeyu Wang
  • Jun Yuan

Abstract

We show how D4PG can be used in conjunction with quantile regression to develop a hedging strategy for a trader responsible for derivatives that arrive stochastically and depend on a single underlying asset. We assume that the trader makes the portfolio delta neutral at the end of each day by taking a position in the underlying asset. We focus on how trades in the options can be used to manage gamma and vega. The option trades are subject to transaction costs. We consider three different objective functions. We reach conclusions on how the optimal hedging strategy depends on the trader's objective function, the level of transaction costs, and the maturity of the options used for hedging. We also investigate the robustness of the hedging strategy to the process assumed for the underlying asset.

Suggested Citation

  • Jay Cao & Jacky Chen & Soroush Farghadani & John Hull & Zissis Poulos & Zeyu Wang & Jun Yuan, 2022. "Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning," Papers 2205.05614, arXiv.org, revised Jan 2023.
    • Handle: RePEc:arx:papers:2205.05614
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    References listed on IDEAS

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    1. Saeed Marzban & Erick Delage & Jonathan Yumeng Li, 2020. "Equal Risk Pricing and Hedging of Financial Derivatives with Convex Risk Measures," Papers 2002.02876, arXiv.org, revised Sep 2020.
    2. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments," Papers 2102.12694, arXiv.org.
    3. Robert C. Merton, 2005. "Theory of rational option pricing," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 8, pages 229-288, World Scientific Publishing Co. Pte. Ltd..
    4. 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.
    5. Alexandre Carbonneau & Frédéric Godin, 2021. "Equal risk pricing of derivatives with deep hedging," Quantitative Finance, Taylor & Francis Journals, vol. 21(4), pages 593-608, April.
    6. 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.
    7. Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Hedging of Derivatives Using Reinforcement Learning," Papers 2103.16409, arXiv.org.
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    Cited by:

    1. Anil Sharma & Freeman Chen & Jaesun Noh & Julio DeJesus & Mario Schlener, 2024. "Hedging and Pricing Structured Products Featuring Multiple Underlying Assets," Papers 2411.01121, arXiv.org.
    2. Reilly Pickard & Finn Wredenhagen & Julio DeJesus & Mario Schlener & Yuri Lawryshyn, 2024. "Hedging American Put Options with Deep Reinforcement Learning," Papers 2405.06774, arXiv.org.
    3. Parvin Malekzadeh & Zissis Poulos & Jacky Chen & Zeyu Wang & Konstantinos N. Plataniotis, 2024. "EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning," Papers 2408.12446, arXiv.org, revised Aug 2024.
    4. Reilly Pickard & F. Wredenhagen & Y. Lawryshyn, 2024. "Optimizing Deep Reinforcement Learning for American Put Option Hedging," Papers 2405.08602, arXiv.org.

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