Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information
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Abstract (arXiv)
We present a dynamic hedging scheme for S&P 500 options, where rebalancing decisions are enhanced by integrating information about the implied volatility surface dynamics. The optimal hedging strategy is obtained through a deep policy gradient-type reinforcement learning algorithm. The favorable inclusion of forward-looking information embedded in the volatility surface allows our procedure to outperform several conventional benchmarks such as practitioner and smiled-implied delta hedging procedures, both in simulation and backtesting experiments. The outperformance is more pronounced in the presence of transaction costs.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 72 references that are in the map. First posted on arXiv 30 Jul 2024.
Builds on
10 of 72 references.
References · 10
- 2018Deep Hedgingcited by 106
- 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
- 2023Quantum Deep Hedgingcited by 22
- 2013An empirical analysis of dropout in piecewise linear networkscited by 19
- 2019Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGANcited by 16
- 2022Gamma and Vega Hedging Using Deep Distributional Reinforcement Learningcited by 15
- 2021Deep Hedging under Rough Volatilitycited by 10
- 2023Robust Risk-Aware Option Hedgingcited by 9
- 2018The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolioscited by 8
- 2024Deep Hedging with Market Impactcited by 4
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Citing papers per year: 2025: 2.
2 papers.
- 2025Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedgingcited by 0
- 2025Learning to Hedge Swaptionscited by 0
- 2025Learning to Hedge Swaptions7 Dec 2025
- 2025Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging10 Oct 2025