# Deep Hedging under Rough Volatility

arXiv: https://arxiv.org/abs/2102.01962 · HTML: https://mlsanity.com/paper/2102.01962/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2021.

## Built on by (most cited first, 10 of 10)

- [Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer](https://mlsanity.com/paper/2201.13094/) (2022)
- [Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions](https://mlsanity.com/paper/2207.07467/) (2022)
- [SigFormer: Signature Transformers for Deep Hedging](https://mlsanity.com/paper/2310.13369/) (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)
- [Hedging with memory: shallow and deep learning with signatures](https://mlsanity.com/paper/2508.02759/) (2025)
- [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)
- [Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators](https://mlsanity.com/paper/2404.09462/) (2024)
- [NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes](https://mlsanity.com/paper/2607.14361/) (2026)

## Abstract (arXiv)

We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. Secondly, we analyse the hedging behaviour in these models in terms of P&L distributions and draw comparisons to jump diffusion models if the the rebalancing frequency is realistically small.