High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach

Kang Gao, Perukrishnen Vytelingum, Stephen Weston, Wayne Luk and Ce Guo

Papers from arXiv.org

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 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, aiming at precipitating 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: 2022-08
New Economics Papers: this item is included in nep-cmp, nep-hme and nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Artificial Societies and Social Simulation 2024 27 (2) 8

Downloads: (external link)
http://arxiv.org/pdf/2208.13654 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2208.13654

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().