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A Topological Approach to Parameterizing Deep Hedging Networks

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  • Alok Das
  • Kiseop Lee

Abstract

Deep hedging uses recurrent neural networks to hedge financial products that cannot be fully hedged in incomplete markets. Previous work in this area focuses on minimizing some measure of quadratic hedging error by calculating pathwise gradients, but doing so requires large batch sizes and can make training effective models in a reasonable amount of time challenging. We show that by adding certain topological features, we can reduce batch sizes substantially and make training these models more practically feasible without greatly compromising hedging performance.

Suggested Citation

  • Alok Das & Kiseop Lee, 2025. "A Topological Approach to Parameterizing Deep Hedging Networks," Papers 2510.16938, arXiv.org.
    • Handle: RePEc:arx:papers:2510.16938
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    References listed on IDEAS

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    1. Alexandre Carbonneau, 2020. "Deep Hedging of Long-Term Financial Derivatives," Papers 2007.15128, arXiv.org.
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    4. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    5. 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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    7. Andrei Neagu & Fr'ed'eric Godin & Clarence Simard & Leila Kosseim, 2024. "Deep Hedging with Market Impact," Papers 2402.13326, arXiv.org, revised Feb 2024.
    8. Bernhard Hientzsch, 2023. "Reinforcement Learning and Deep Stochastic Optimal Control for Final Quadratic Hedging," Papers 2401.08600, arXiv.org.
    9. Anish Rai & Buddha Nath Sharma & Salam Rabindrajit Luwang & Md. Nurujjaman & Sushovan Majhi, 2024. "Identifying Extreme Events in the Stock Market: A Topological Data Analysis," Papers 2405.16052, arXiv.org.
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