# Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management

arXiv: https://arxiv.org/abs/2502.17777 · HTML: https://mlsanity.com/paper/2502.17777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## 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, 9 of 9)

- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [Distributional Reinforcement Learning with Quantile Regression](https://mlsanity.com/paper/1710.10044/) (2017)
- [Implicit Quantile Networks for Distributional Reinforcement Learning](https://mlsanity.com/paper/1806.06923/) (2018)
- [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://mlsanity.com/paper/1706.10059/) (2017)
- [Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors](https://mlsanity.com/paper/2001.02811/) (2020)
- [Distributional Policy Optimization: An Alternative Approach for Continuous Control](https://mlsanity.com/paper/1905.09855/) (2019)
- [Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning](https://mlsanity.com/paper/2205.05614/) (2022)
- [Pricing options and computing implied volatilities using neural networks](https://mlsanity.com/paper/1901.08943/) (2019)
- [Model Uncertainty, Recalibration, and the Emergence of Delta-Vega Hedging](https://mlsanity.com/paper/1704.04524/) (2017)

## Abstract (arXiv)

In the field of financial derivatives trading, managing volatility risk is crucial for protecting investment portfolios from market changes. Traditional Vega hedging strategies, which often rely on basic and rule-based models, are hard to adapt well to rapidly changing market conditions. We introduce a new framework for dynamic Vega hedging, the Adaptive Nesterov Accelerated Distributional Deep Hedging (ANADDH), which combines distributional reinforcement learning with a tailored design based on adaptive Nesterov acceleration. This approach improves the learning process in complex financial environments by modeling the hedging efficiency distribution, providing a more accurate and responsive hedging strategy. The design of adaptive Nesterov acceleration refines gradient momentum adjustments, significantly enhancing the stability and speed of convergence of the model. Through empirical analysis and comparisons, our method demonstrates substantial performance gains over existing hedging techniques. Our results confirm that this innovative combination of distributional reinforcement learning with the proposed optimization techniques improves financial risk management and highlights the practical benefits of implementing advanced neural network architectures in the finance sector.