Simulation and estimation of an agent-based market-model with a matching engine

TL;DR

This paper develops a realistic agent-based market simulation using a matching engine with asynchronous, event-driven interactions, enabling better modeling of price impacts and market dynamics.

Contribution

It introduces a modified matching engine infrastructure for agent-based models, enhancing realism by incorporating asynchronous, event-time interactions and reactive agent behavior.

Findings

Realistic price impact curves generated from simulations

Enhanced market behavior visualization and stylized facts

Reactive agent interactions improve model simplicity and accuracy

Abstract

An agent-based model with interacting low frequency liquidity takers inter-mediated by high-frequency liquidity providers acting collectively as market makers can be used to provide realistic simulated price impact curves. This is possible when agent-based model interactions occur asynchronously via order matching using a matching engine in event time to replace sequential calendar time market clearing. Here the matching engine infrastructure has been modified to provide a continuous feed of order confirmations and updates as message streams in order to conform more closely to live trading environments. The resulting trade and quote message data from the simulations are then aggregated, calibrated and visualised. Various stylised facts are presented along with event visualisations and price impact curves. We argue that additional realism in modelling can be achieved with a small set of agent parameters and simple interaction rules once interactions are reactive, asynchronous and in event time. We argue that the reactive nature of market agents may be a fundamental property of financial markets and when accounted for can allow for parsimonious modelling without recourse to additional sources of noise.