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

arXiv: https://arxiv.org/abs/2410.11789 · HTML: https://mlsanity.com/paper/2410.11789/

## 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.

## 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, 10 of 10)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Reinforcement Learning in Economics and Finance](https://mlsanity.com/paper/2003.10014/) (2020)
- [Market Making via Reinforcement Learning](https://mlsanity.com/paper/1804.04216/) (2018)
- [Reinforcement Learning for Market Making in a Multi-agent Dealer Market](https://mlsanity.com/paper/1911.05892/) (2019)
- [Deep Hedging of Derivatives Using Reinforcement Learning](https://mlsanity.com/paper/2103.16409/) (2021)
- [Robust Market Making via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2003.01820/) (2020)
- [Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions](https://mlsanity.com/paper/2207.07467/) (2022)

## 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.