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Market Microstructure Design and Flash Crashes: A Simulation Approach

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  • Paul Brewer
  • Jaksa Cvitanic
  • Charles R. Plott

Abstract

We study consequences of regulatory interventions in limit order markets that aim at stabilizing the market after an occurrence of a “flash crash”. We use a simulation platform that creates random arrivals of trade orders, that allows us to analyze subtle theoretical features of liquidity and price variability under various market structures. The simulations are performed under continuous double-auction microstructure, and under alternatives, including imposing minimum resting times, shutting off trading for a period of time, and switching to call auction mechanisms. We find that the latter is the most effective in restoring the liquidity of the book and recovery of the price level. However, one has to be cautious about possible consequences of the intervention on the traders' strategies, including an undesirable slowdown of a convergence to a new equilibrium after a change in fundamentals.

Suggested Citation

  • Paul Brewer & Jaksa Cvitanic & Charles R. Plott, 2013. "Market Microstructure Design and Flash Crashes: A Simulation Approach," Journal of Applied Economics, Taylor & Francis Journals, vol. 16(2), pages 223-250, November.
    • Handle: RePEc:taf:recsxx:v:16:y:2013:i:2:p:223-250
    DOI: 10.1016/S1514-0326(13)60010-0
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    Citations

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    Cited by:

    1. Leal, Sandrine Jacob & Napoletano, Mauro, 2019. "Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 15-41.
    2. Iryna Veryzhenko & Arthur Jonath & Etienne Harb, 2020. "Non-Value-Added Tax to Improve Market Fairness," Working Papers hal-02881064, HAL.
    3. Iryna Veryzhenko & Arthur Jonath & Etienne Harb, 2022. "Non-Value-Added Tax to improve market fairness and quality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-30, December.
    4. Silvio John Camilleri, 2015. "The Impact of Stock Market Structure on Volatility: Evidence from a Call Auction Suspension," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 6(2), pages 44-53, April.
    5. Nathalie Oriol & Iryna Veryzhenko, 2019. "Market structure or traders' behavior? A multi agent model to assess flash crash phenomena and their regulation," Quantitative Finance, Taylor & Francis Journals, vol. 19(7), pages 1075-1092, July.
    6. Nathalie Oriol & Iryna Veryzhenko, 2015. "Market structure or traders’ behavior? An assessment of flash crash phenomena and their regulation based on a multi-agent simulation," Working Papers halshs-01254435, HAL.
    7. Antonio Briola & Silvia Bartolucci & Tomaso Aste, 2024. "Deep Limit Order Book Forecasting," Papers 2403.09267, arXiv.org, revised Mar 2024.
    8. repec:hal:spmain:info:hdl:2441/6ummnc8nko827b2luohnctekk7 is not listed on IDEAS
    9. repec:hal:spmain:info:hdl:2441/3utlh0ehcn860pus6p2p683ade is not listed on IDEAS
    10. Qixuan Luo & Yu Shi & Xuan Zhou & Handong Li, 2021. "Research on the Effects of Institutional Liquidation Strategies on the Market Based on Multi-agent Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1025-1049, December.
    11. Brewer, Paul & Ratan, Anmol, 2019. "Profitability, efficiency, and inequality in double auction markets with snipers," Journal of Economic Behavior & Organization, Elsevier, vol. 164(C), pages 486-499.
    12. Iwao Maeda & David deGraw & Michiharu Kitano & Hiroyasu Matsushima & Hiroki Sakaji & Kiyoshi Izumi & Atsuo Kato, 2020. "Deep Reinforcement Learning in Agent Based Financial Market Simulation," JRFM, MDPI, vol. 13(4), pages 1-17, April.
    13. Sandrine Jacob Leal & Mauro Napoletano, 2017. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High-Frequency Trading," Post-Print hal-01768876, HAL.

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