# Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling

arXiv: https://arxiv.org/abs/2307.13217 · HTML: https://mlsanity.com/paper/2307.13217/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Quant GANs: Deep Generation of Financial Time Series](https://mlsanity.com/paper/1907.06673/) (2019)
- [Generating Realistic Stock Market Order Streams](https://mlsanity.com/paper/2006.04212/) (2020)
- [Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions](https://mlsanity.com/paper/2207.07467/) (2022)
- [Deep Hedging under Rough Volatility](https://mlsanity.com/paper/2102.01962/) (2021)
- [Fractional SDE-Net: Generation of Time Series Data with Long-term Memory](https://mlsanity.com/paper/2201.05974/) (2022)
- [Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets](https://mlsanity.com/paper/2204.13338/) (2022)
- [Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction](https://mlsanity.com/paper/2106.04114/) (2021)

## Built on by (most cited first, 5 of 5)

- [SoK: Security of Autonomous LLM Agents in Agentic Commerce](https://mlsanity.com/paper/2604.15367/) (2026)
- [Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging](https://mlsanity.com/paper/2510.09247/) (2025)
- [Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators](https://mlsanity.com/paper/2404.09462/) (2024)
- [Climate-Dyna Deep Hedging for XVAs: Model-Based Reinforcement Learning, Residual Climate HVA, and Hedge-Instrument Discovery](https://mlsanity.com/paper/2608.01208/) (2026)
- [Taming the Greeks: Option Portfolios with Inductive Biases](https://mlsanity.com/paper/2609.33767/) (2026)

## Abstract (arXiv)

Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market conditions, such as market frictions, which are challenging to address within the traditional mathematical finance framework. Since deep hedging relies on market simulation, the underlying asset price process model is crucial. However, existing literature on deep hedging often relies on traditional mathematical finance models, e.g., Brownian motion and stochastic volatility models, and discovering effective underlying asset models for deep hedging learning has been a challenge. In this study, we propose a new framework called adversarial deep hedging, inspired by adversarial learning. In this framework, a hedger and a generator, which respectively model the underlying asset process and the underlying asset process, are trained in an adversarial manner. The proposed method enables to learn a robust hedger without explicitly modeling the underlying asset process. Through numerical experiments, we demonstrate that our proposed method achieves competitive performance to models that assume explicit underlying asset processes across various real market data.