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

TL;DR

This paper introduces a novel approach using Deep Reinforcement Learning, specifically DDPG and SAC algorithms, to improve the dynamic volatility fitting process in equity derivatives, enabling better adaptation to market shifts.

Contribution

It applies advanced DRL algorithms to the volatility fitting problem, demonstrating comparable or superior performance to traditional methods and highlighting the framework's suitability for complex, online learning tasks.

Findings

DRL algorithms achieve at least as good results as standard methods

Reinforcement learning handles complex objective functions effectively

Framework is suitable for online adaptation in market regimes

Abstract

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.