Event-Based Limit Order Book Simulation under a Neural Hawkes Process: Application in Market-Making

TL;DR

This paper introduces a neural Hawkes process-based event-driven limit order book model that accurately captures high-frequency market dynamics and improves market-making strategies through realistic simulation of order flows.

Contribution

It presents a novel neural Hawkes process model for LOB simulation that effectively captures complex event interactions and enhances reinforcement learning-based market-making.

Findings

Model captures key market volatility characteristics

Simulated trade fills closely match real data

Improves market-making strategy performance

Abstract

In this paper, we propose an event-driven Limit Order Book (LOB) model that captures twelve of the most observed LOB events in exchange-based financial markets. To model these events, we propose using the state-of-the-art Neural Hawkes process, a more robust alternative to traditional Hawkes process models. More specifically, this model captures the dynamic relationships between different event types, particularly their long- and short-term interactions, using a Long Short-Term Memory neural network. Using this framework, we construct a midprice process that captures the event-driven behavior of the LOB by simulating high-frequency dynamics like how they appear in real financial markets. The empirical results show that our model captures many of the broader characteristics of the price fluctuations, particularly in terms of their overall volatility. We apply this LOB simulation model within a Deep Reinforcement Learning Market-Making framework, where the trading agent can now complete trade order fills in a manner that closely resembles real-market trade execution. Here, we also compare the results of the simulated model with those from real data, highlighting how the overall performance and the distribution of trade order fills closely align with the same analysis on real data.