Over-the-Counter Market Making via Reinforcement Learning
Abstract
The over-the-counter (OTC) market is characterized by a unique feature that allows market makers to adjust bid-ask spreads based on order size. However, this flexibility introduces complexity, transforming the market-making problem into a high-dimensional stochastic control problem that presents significant challenges. To address this, this paper proposes an innovative solution utilizing reinforcement learning techniques to tackle the OTC market-making problem. By assuming a linear inverse relationship between market order arrival intensity and bid-ask spreads, we demonstrate the optimal policy for bid-ask spreads follows a Gaussian distribution. We apply two reinforcement learning algorithms to conduct a numerical analysis, revealing the resulting return distribution and bid-ask spreads under different time and inventory levels.
Cite
@article{arxiv.2307.01816,
title = {Over-the-Counter Market Making via Reinforcement Learning},
author = {Zhou Fang and Haiqing Xu},
journal= {arXiv preprint arXiv:2307.01816},
year = {2023}
}