mlsanityData to 14 Oct 2026

Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management

arXiv 2502.17777 ↗First posted 25 Feb 2025cs.LG q-fin.CPcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

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.

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.

Builds on

9 of 31 references.

References · 9

  1. 2017A Distributional Perspective on Reinforcement Learningcited by 765
  2. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 474
  3. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
  4. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  5. 2020Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errorscited by 85
  6. 2019Distributional Policy Optimization: An Alternative Approach for Continuous Controlcited by 22
  7. 2022Gamma and Vega Hedging Using Deep Distributional Reinforcement Learningcited by 15
  8. 2019Pricing options and computing implied volatilities using neural networkscited by 9
  9. 2017Model Uncertainty, Recalibration, and the Emergence of Delta-Vega Hedgingcited by 2

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