# Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions

arXiv: https://arxiv.org/abs/2207.07467 · HTML: https://mlsanity.com/paper/2207.07467/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 15 Jul 2022.

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

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Distributional Reinforcement Learning with Quantile Regression](https://mlsanity.com/paper/1710.10044/) (2017)
- [Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach](https://mlsanity.com/paper/1506.02188/) (2015)
- [Recent Advances in Reinforcement Learning in Finance](https://mlsanity.com/paper/2112.04553/) (2021)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Algorithms for CVaR Optimization in MDPs](https://mlsanity.com/paper/1406.3339/) (2014)
- [Multi-Asset Spot and Option Market Simulation](https://mlsanity.com/paper/2112.06823/) (2021)
- [Option Hedging with Risk Averse Reinforcement Learning](https://mlsanity.com/paper/2010.12245/) (2020)
- [Risk-Aware Transfer in Reinforcement Learning using Successor Features](https://mlsanity.com/paper/2105.14127/) (2021)
- [Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures](https://mlsanity.com/paper/2111.07844/) (2021)
- [Deep Hedging under Rough Volatility](https://mlsanity.com/paper/2102.01962/) (2021)

## Built on by (most cited first, 13 of 13)

- [Quantum Deep Hedging](https://mlsanity.com/paper/2303.16585/) (2023)
- [Fast Deep Hedging with Second-Order Optimization](https://mlsanity.com/paper/2410.22568/) (2024)
- [Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling](https://mlsanity.com/paper/2307.13217/) (2023)
- [The Formalism-Implementation Gap in Reinforcement Learning Research](https://mlsanity.com/paper/2510.16175/) (2025)
- [Applying Reinforcement Learning to Option Pricing and Hedging](https://mlsanity.com/paper/2310.04336/) (2023)
- [Hedging American Put Options with Deep Reinforcement Learning](https://mlsanity.com/paper/2405.06774/) (2024)
- [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)
- [Generating Financial Time Series by Matching Random Convolutional Features](https://mlsanity.com/paper/2606.05138/) (2026)
- [Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators](https://mlsanity.com/paper/2404.09462/) (2024)
- [EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning](https://mlsanity.com/paper/2408.12446/) (2024)
- [Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2410.11789/) (2024)
- [Risk-Averse Reinforcement Learning with Itakura-Saito Loss](https://mlsanity.com/paper/2505.16925/) (2025)

## Abstract (arXiv)

We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states. We develop a novel actor-critic algorithm for solving general risk-averse stochastic control problems and use it to learn hedging strategies across multiple risk aversion levels simultaneously. We demonstrate the effectiveness of the approach with a numerical example in a stochastic volatility environment.