Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality

TL;DR

This paper introduces a deep reinforcement learning approach to approximate optimal market making strategies across many bonds, overcoming the computational challenges of high-dimensional models.

Contribution

It develops a novel actor-critic deep neural network method tailored for high-dimensional multi-asset market making problems inspired by Avellaneda-Stoikov.

Findings

Effective approximation of optimal quotes in high-dimensional bond markets.

Overcomes the curse of dimensionality with a neural network-based approach.

Provides a scalable solution for multi-asset market making.

Abstract

In corporate bond markets, which are mainly OTC markets, market makers play a central role by providing bid and ask prices for a large number of bonds to asset managers from all around the globe. Determining the optimal bid and ask quotes that a market maker should set for a given universe of bonds is a complex task. Useful models exist, most of them inspired by that of Avellaneda and Stoikov. These models describe the complex optimization problem faced by market makers: proposing bid and ask prices in an optimal way for making money out of the difference between bid and ask prices while mitigating the market risk associated with holding inventory. While most of the models only tackle one-asset market making, they can often be generalized to a multi-asset framework. However, the problem of solving numerically the equations characterizing the optimal bid and ask quotes is seldom tackled in the literature, especially in high dimension. In this paper, our goal is to propose a numerical method for approximating the optimal bid and ask quotes over a large universe of bonds in a model \`a la Avellaneda-Stoikov. Because we aim at considering a large universe of bonds, classical finite difference methods as those discussed in the literature cannot be used and we present therefore a discrete-time method inspired by reinforcement learning techniques. More precisely, the approach we propose is a model-based actor-critic-like algorithm involving deep neural networks.