# Robust Risk-Aware Option Hedging

arXiv: https://arxiv.org/abs/2303.15216 · HTML: https://mlsanity.com/paper/2303.15216/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Neural networks for option pricing and hedging: a literature review](https://mlsanity.com/paper/1911.05620/) (2019)
- [Deep Hedging of Derivatives Using Reinforcement Learning](https://mlsanity.com/paper/2103.16409/) (2021)
- [Robust pricing and hedging via neural SDEs](https://mlsanity.com/paper/2007.04154/) (2020)
- [Reinforcement Learning with Dynamic Convex Risk Measures](https://mlsanity.com/paper/2112.13414/) (2021)
- [Robust Risk-Aware Reinforcement Learning](https://mlsanity.com/paper/2108.10403/) (2021)
- [Sensitivity analysis of Wasserstein distributionally robust optimization problems](https://mlsanity.com/paper/2006.12022/) (2020)
- [Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning](https://mlsanity.com/paper/2206.14666/) (2022)

## Built on by (most cited first, 9 of 9)

- [Deep Hedging with Market Impact](https://mlsanity.com/paper/2402.13326/) (2024)
- [Robust Reinforcement Learning with Dynamic Distortion Risk Measures](https://mlsanity.com/paper/2409.10096/) (2024)
- [Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradients](https://mlsanity.com/paper/2406.15612/) (2024)
- [Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information](https://mlsanity.com/paper/2407.21138/) (2024)
- [Distributional Adversarial Attacks and Training in Deep Hedging](https://mlsanity.com/paper/2508.14757/) (2025)
- [Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling](https://mlsanity.com/paper/2506.07299/) (2025)
- [Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging](https://mlsanity.com/paper/2510.09247/) (2025)
- [Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets](https://mlsanity.com/paper/2510.19950/) (2025)
- [Robust Control under Stationary Ambiguity](https://mlsanity.com/paper/2608.04832/) (2026)

## Abstract (arXiv)

The objectives of option hedging/trading extend beyond mere protection against downside risks, with a desire to seek gains also driving agent's strategies. In this study, we showcase the potential of robust risk-aware reinforcement learning (RL) in mitigating the risks associated with path-dependent financial derivatives. We accomplish this by leveraging a policy gradient approach that optimises robust risk-aware performance criteria. We specifically apply this methodology to the hedging of barrier options, and highlight how the optimal hedging strategy undergoes distortions as the agent moves from being risk-averse to risk-seeking. As well as how the agent robustifies their strategy. We further investigate the performance of the hedge when the data generating process (DGP) varies from the training DGP, and demonstrate that the robust strategies outperform the non-robust ones.