mlsanityData to 14 Oct 2026

Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach

arXiv 2410.11789 ↗First posted 15 Oct 2024q-fin.CP math.OC math.PRcited by 0

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

Abstract (arXiv)

The volatility fitting is one of the core problems in the equity derivatives business. Through a set of deterministic rules, the degrees of freedom in the implied volatility surface encoding (parametrization, density, diffusion) are defined. Whilst very effective, this approach widespread in the industry is not natively tailored to learn from shifts in market regimes and discover unsuspected optimal behaviors. In this paper, we change the classical paradigm and apply the latest advances in Deep Reinforcement Learning(DRL) to solve the fitting problem. In particular, we show that variants of Deep Deterministic Policy Gradient (DDPG) and Soft Actor Critic (SAC) can achieve at least as good as standard fitting algorithms. Furthermore, we explain why the reinforcement learning framework is appropriate to handle complex objective functions and is natively adapted for online learning.

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 10 of its 35 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

10 of 35 references.

References · 10

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  5. 2020Reinforcement Learning in Economics and Financecited by 58
  6. 2018Market Making via Reinforcement Learningcited by 47
  7. 2019Reinforcement Learning for Market Making in a Multi-agent Dealer Marketcited by 39
  8. 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
  9. 2020Robust Market Making via Adversarial Reinforcement Learningcited by 14
  10. 2022Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversionscited by 13

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