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Effects of Limit Order Book Information Level on Market Stability Metrics

Author

Listed:
  • Mark Paddrik

    (Office of Financial Research)

  • Roy Hayes

    (University of Virginia)

  • William Scherer

    (University of Virginia)

  • Peter Beling

    (University of Virginia)

Abstract

Using an agent-based model of the limit order book, we explore how the levels of information available to participants, exchanges, and regulators can be used to improve our understanding of the stability and resiliency of a market. Ultimately, we want to know if electronic market data contains previously undetected information that could allow us to better assess market stability. Using data produced in the controlled environment of an agent-based model's limit order book, we examine various resiliency indicators to determine their predictive capabilities. Most of the types of data created have traditionally been available either publicly or on a restricted basis to regulators and exchanges, but other types have never been collected. We confirmed our findings using actual order flow data with user identifications included from the CME (Chicago Mercantile Exchange) and New York Mercantile Exchange (NYMEX). Our findings strongly suggest that high-fidelity microstructure data in combination with price data can be used to define stability indicators capable of reliably signaling a high likelihood for an imminent flash crash event about one minute before it occurs.

Suggested Citation

  • Mark Paddrik & Roy Hayes & William Scherer & Peter Beling, 2014. "Effects of Limit Order Book Information Level on Market Stability Metrics," Working Papers 14-09, Office of Financial Research, US Department of the Treasury.
  • Handle: RePEc:ofr:wpaper:14-09
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    Cited by:

    1. Mahmoud Mahfouz & Angelos Filos & Cyrine Chtourou & Joshua Lockhart & Samuel Assefa & Manuela Veloso & Danilo Mandic & Tucker Balch, 2019. "On the Importance of Opponent Modeling in Auction Markets," Papers 1911.12816, arXiv.org.
    2. Matteo G. Richiardi, 2017. "The Future of Agent-Based Modeling," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 271-287, March.
    3. Paulin, James & Calinescu, Anisoara & Wooldridge, Michael, 2019. "Understanding flash crash contagion and systemic risk: A micro–macro agent-based approach," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 200-229.
    4. James Paulin & Anisoara Calinescu & Michael Wooldridge, 2018. "Understanding Flash Crash Contagion and Systemic Risk: A Micro-Macro Agent-Based Approach," Papers 1805.08454, arXiv.org.
    5. Richard Bookstaber & Mark Paddrik, 2015. "An Agent-Based Model of Liquidity," Working Papers 15-18, Office of Financial Research, US Department of the Treasury.
    6. Rainer Alt, 2020. "Electronic Markets on sustainability," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 667-674, December.
    7. Mahmoud Mahfouz & Tucker Balch & Manuela Veloso & Danilo Mandic, 2021. "Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets," Papers 2110.01325, arXiv.org, revised Oct 2021.

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    Keywords

    Limit Order Book; Market Stability;

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