# Deep Hedging with Market Impact

arXiv: https://arxiv.org/abs/2402.13326 · HTML: https://mlsanity.com/paper/2402.13326/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 20 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Double Deep Q-Learning for Optimal Execution](https://mlsanity.com/paper/1812.06600/) (2018)
- [Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning](https://mlsanity.com/paper/2205.05614/) (2022)
- [Robust Risk-Aware Option Hedging](https://mlsanity.com/paper/2303.15216/) (2023)
- [Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market](https://mlsanity.com/paper/1811.02880/) (2018)

## Built on by (most cited first, 4 of 4)

- [Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information](https://mlsanity.com/paper/2407.21138/) (2024)
- [Deep Reinforcement Learning Algorithms for Option Hedging](https://mlsanity.com/paper/2504.05521/) (2025)
- [Robust and Efficient Deep Hedging via Linearized Objective Neural Network](https://mlsanity.com/paper/2502.17757/) (2025)
- [Autonomous AI Agents for Option Hedging: Enhancing Financial Stability through Shortfall Aware Reinforcement Learning](https://mlsanity.com/paper/2603.06587/) (2026)

## Abstract (arXiv)

Dynamic hedging is the practice of periodically transacting financial instruments to offset the risk caused by an investment or a liability. Dynamic hedging optimization can be framed as a sequential decision problem; thus, Reinforcement Learning (RL) models were recently proposed to tackle this task. However, existing RL works for hedging do not consider market impact caused by the finite liquidity of traded instruments. Integrating such feature can be crucial to achieve optimal performance when hedging options on stocks with limited liquidity. In this paper, we propose a novel general market impact dynamic hedging model based on Deep Reinforcement Learning (DRL) that considers several realistic features such as convex market impacts, and impact persistence through time. The optimal policy obtained from the DRL model is analysed using several option hedging simulations and compared to commonly used procedures such as delta hedging. Results show our DRL model behaves better in contexts of low liquidity by, among others: 1) learning the extent to which portfolio rebalancing actions should be dampened or delayed to avoid high costs, 2) factoring in the impact of features not considered by conventional approaches, such as previous hedging errors through the portfolio value, and the underlying asset's drift (i.e. the magnitude of its expected return).