Market Simulations with a Matching Engine

We demonstrate the CoinTossX Java web-application as a low-latency, high-throughput, open-source matching engine/artificial exchange/simulation platform and deploy it to a cloud environment for asynchronous order matching and submission in a controlled framework via two seperate simulation techniques — Hawkes processes and agent-based modelling. A 10-variate Hawkes model stress tests the software whilst measuring the extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a continuous-double auction. Estimation and calibration to the subsequent trade-and-quote data results in a model specification statistically different from the original — providing insight into the limits of the software, inference conducted on HFT models and future market microstructure modelling considerations. An asynchronous ABM with interacting low-frequency liquidity takers and high-frequency liquidity-providers is subsequently formulated with the aim of producing realistic trading scenarios/price action without relying on restrictive modelling assumptions or additional sources of noise. The resulting simulations are shown to replicate many stylized facts along with non-trivial price-impact curves and we use this to argue for future simple, reactive/actor-based financial model specifications that mimics real-world work-flow and system implementation.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2022

Author dc:creator

• Jericevich, Ivan

Advisor dc:contributor.advisor

• Gebbie, Timothy

• Statistical Sciences

Handle dc:identifier.uri

http://hdl.handle.net/11427/37141

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/37141