High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach
File(s)
Published version
Author(s)
Gao, Kang
Vytelingum, Perukrishnen
Weston, Stephen
Luk, Wayne
Guo, Ce
Type
Journal Article
Abstract
This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place on that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, replicating real-world scenarios that could precipitate mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers.
Date Issued
2024-04-01
Date Acceptance
2024-03-22
Citation
Journal of Artificial Societies and Social Simulation, 2024, 27 (2)
ISSN
1460-7425
Publisher
European Social Simulation Association
Journal / Book Title
Journal of Artificial Societies and Social Simulation
Volume
27
Issue
2
Copyright Statement
All work published in JASSS is licensed under a Creative Commons Attribution 4.0 International License.
License URL
Identifier
http://dx.doi.org/10.18564/jasss.5403
Publication Status
Published
Article Number
8
Date Publish Online
2024-03-31