Distributional Adversarial Attacks and Training in Deep Hedging
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Abstract (arXiv)
In this paper, we study the robustness of classical deep hedging strategies under distributional shifts by leveraging the concept of adversarial attacks. We first demonstrate that standard deep hedging models are highly vulnerable to small perturbations in the input distribution, resulting in significant performance degradation. Motivated by this, we propose an adversarial training framework tailored to increase the robustness of deep hedging strategies. Our approach extends pointwise adversarial attacks to the distributional setting and introduces a computationally tractable reformulation of the adversarial optimization problem over a Wasserstein ball. This enables the efficient training of hedging strategies that are resilient to distributional perturbations. Through extensive numerical experiments, we show that adversarially trained deep hedging strategies consistently outperform their classical counterparts in terms of out-of-sample performance and resilience to model misspecification. Additional results indicate that the robust strategies maintain reliable performance on real market data and remain effective during periods of market change. Our findings establish a practical and effective framework for robust deep hedging under realistic market uncertainties.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 20 Aug 2025.
Builds on
23 of 50 references.
References · 23
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11 more
- 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
- 2023Quantum Deep Hedgingcited by 22
- 2023Wasserstein distributional robustness of neural networkscited by 16
- 2020Option Hedging with Risk Averse Reinforcement Learningcited by 13
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- 2021Robust Generalization despite Distribution Shift via Minimum Discriminating Informationcited by 6
- 2024Fast Deep Hedging with Second-Order Optimizationcited by 6
- 2020Hedging with Linear Regressions and Neural Networkscited by 2
- 2024Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Trainingcited by 2
- 2025Wasserstein distributional adversarial training for deep neural networkscited by 1
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Citing papers per year: 2026: 2.
2 papers.