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A Multi Agent Model for the Limit Order Book Dynamics

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  • Marco Bartolozzi

Abstract

In the present work we introduce a novel multi-agent model with the aim to reproduce the dynamics of a double auction market at microscopic time scale through a faithful simulation of the matching mechanics in the limit order book. The agents follow a noise decision making process where their actions are related to a stochastic variable, "the market sentiment", which we define as a mixture of public and private information. The model, despite making just few basic assumptions over the trading strategies of the agents, is able to reproduce several empirical features of the high-frequency dynamics of the market microstructure not only related to the price movements but also to the deposition of the orders in the book.

Suggested Citation

  • Marco Bartolozzi, 2010. "A Multi Agent Model for the Limit Order Book Dynamics," Papers 1005.0182, arXiv.org, revised Oct 2010.
  • Handle: RePEc:arx:papers:1005.0182
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    References listed on IDEAS

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    Cited by:

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    2. 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.
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    4. Alex Langnau & Yanko Punchev, 2011. "Stochastic Price Dynamics Implied By the Limit Order Book," Papers 1105.4789, arXiv.org.
    5. 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.
    6. Iris Lucas & Michel Cotsaftis & Cyrille Bertelle, 2017. "Heterogeneity and Self-Organization of Complex Systems Through an Application to Financial Market with Multiagent Systems," Post-Print hal-02114933, HAL.
    7. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.
    8. Wladimir Ostrovsky, 2023. "Dealer Strategies in Agent-Based Models," Papers 2312.05943, arXiv.org.
    9. repec:hal:spmain:info:hdl:2441/f6h8764enu2lskk9p4oq9ig8k is not listed on IDEAS
    10. Iris Lucas & Michel Cotsaftis & Cyrille Bertelle, 2018. "Self-Organization, Resilience and Robustness of Complex Systems Through an Application to Financial Market from an Agent-Based Approach," Post-Print hal-02114928, HAL.
    11. repec:hal:spmain:info:hdl:2441/6ummnc8nko827b2luohnctekk7 is not listed on IDEAS
    12. Peter Fratrič & Giovanni Sileno & Sander Klous & Tom Engers, 2022. "Manipulation of the Bitcoin market: an agent-based study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    13. Fabio Della Rossa & Lorenzo Giannini & Pietro DeLellis, 2020. "Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.

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