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Deep Hedging under Rough Volatility

Author

Listed:
  • Blanka Horvath

    (ETH Zürich - Department of Mathematics)

  • Josef Teichmann

    (ETH Zurich; Swiss Finance Institute)

  • Zan Zuric

    (Imperial College London - Department of Mathematics)

Abstract
We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. Secondly, we analyse the hedging behaviour in these models in terms of P&L distributions and draw comparisons to jump diffusion models if the the rebalancing frequency is realistically small.

Suggested Citation

  • Blanka Horvath & Josef Teichmann & Zan Zuric, 2021. "Deep Hedging under Rough Volatility," Swiss Finance Institute Research Paper Series 21-88, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2188
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3778043
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    Citations

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    Cited by:

    1. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep Equal Risk Pricing of Financial Derivatives with Multiple Hedging Instruments," Papers 2102.12694, arXiv.org.
    2. Masanori Hirano & Kentaro Imajo & Kentaro Minami & Takuya Shimada, 2023. "Efficient Learning of Nested Deep Hedging using Multiple Options," Papers 2305.12264, arXiv.org.
    3. Daniele Angelini & Matthieu Garcin, 2024. "Market information of the fractional stochastic regularity model," Papers 2409.07159, arXiv.org.
    4. Pascal Franc{c}ois & Genevi`eve Gauthier & Fr'ed'eric Godin & Carlos Octavio P'erez Mendoza, 2024. "Is the difference between deep hedging and delta hedging a statistical arbitrage?," Papers 2407.14736, arXiv.org, revised Oct 2024.
    5. Ofelia Bonesini & Antoine Jacquier & Alexandre Pannier, 2023. "Rough volatility, path-dependent PDEs and weak rates of convergence," Papers 2304.03042, arXiv.org.
    6. Shota Imaki & Kentaro Imajo & Katsuya Ito & Kentaro Minami & Kei Nakagawa, 2021. "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging," Papers 2103.01775, arXiv.org.
    7. 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.
    8. Kang Gao & Stephen Weston & Perukrishnen Vytelingum & Namid R. Stillman & Wayne Luk & Ce Guo, 2023. "Deeper Hedging: A New Agent-based Model for Effective Deep Hedging," Papers 2310.18755, arXiv.org.
    9. John Armstrong & George Tatlow, 2024. "Deep Gamma Hedging," Papers 2409.13567, arXiv.org.
    10. 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.
    11. Mathieu Rosenbaum & Jianfei Zhang, 2021. "Deep calibration of the quadratic rough Heston model," Papers 2107.01611, arXiv.org, revised May 2022.
    12. Phillip Murray & Ben Wood & Hans Buehler & Magnus Wiese & Mikko S. Pakkanen, 2022. "Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions," Papers 2207.07467, arXiv.org.
    13. Masanori Hirano & Kentaro Minami & Kentaro Imajo, 2023. "Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling," Papers 2307.13217, arXiv.org.
    14. Hainaut, Donatien & Casas, Alex, 2024. "Option pricing in the Heston model with Physics inspired neural networks," LIDAM Discussion Papers ISBA 2024002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Donatien Hainaut & Alex Casas, 2024. "Option pricing in the Heston model with physics inspired neural networks," Annals of Finance, Springer, vol. 20(3), pages 353-376, September.
    16. Carsten Chong & Marc Hoffmann & Yanghui Liu & Mathieu Rosenbaum & Gr'egoire Szymanski, 2022. "Statistical inference for rough volatility: Minimax Theory," Papers 2210.01214, arXiv.org, revised Feb 2024.
    17. Beatrice Acciaio & Anastasis Kratsios & Gudmund Pammer, 2022. "Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer," Papers 2201.13094, arXiv.org, revised Mar 2023.

    More about this item

    Keywords

    Imperfect Hedging; Derivatives Pricing; Derivatives Hedging; Deep Learning; Rough Volatility;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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