Option Market Making via Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method for options market making, addressing the complex, high-dimensional challenge of setting bid-ask spreads across various maturities and strikes.

Contribution

It proposes a novel approach combining stochastic policies and reinforcement learning techniques for optimal options market making.

Findings

Effective in managing high-dimensional options trading environments

Improves bid-ask spread setting for multiple maturities and strikes

Demonstrates potential for enhanced market maker profitability

Abstract

Market making of options with different maturities and strikes is a challenging problem due to its highly dimensional nature. In this paper, we propose a novel approach that combines a stochastic policy and reinforcement learning-inspired techniques to determine the optimal policy for posting bid-ask spreads for an options market maker who trades options with different maturities and strikes.