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Simulation and estimation of an agent-based market-model with a matching engine

Author

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  • Ivan Jericevich
  • Patrick Chang
  • Tim Gebbie
Abstract
An agent-based model with interacting low frequency liquidity takers inter-mediated by high-frequency liquidity providers acting collectively as market makers can be used to provide realistic simulated price impact curves. This is possible when agent-based model interactions occur asynchronously via order matching using a matching engine in event time to replace sequential calendar time market clearing. Here the matching engine infrastructure has been modified to provide a continuous feed of order confirmations and updates as message streams in order to conform more closely to live trading environments. The resulting trade and quote message data from the simulations are then aggregated, calibrated and visualised. Various stylised facts are presented along with event visualisations and price impact curves. We argue that additional realism in modelling can be achieved with a small set of agent parameters and simple interaction rules once interactions are reactive, asynchronous and in event time. We argue that the reactive nature of market agents may be a fundamental property of financial markets and when accounted for can allow for parsimonious modelling without recourse to additional sources of noise.

Suggested Citation

  • Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of an agent-based market-model with a matching engine," Papers 2108.07806, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2108.07806
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    References listed on IDEAS

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    Cited by:

    1. Matthew Dicks & Andrew Paskaramoorthy & Tim Gebbie, 2023. "Many learning agents interacting with an agent-based market model," Papers 2303.07393, arXiv.org, revised Aug 2024.
    2. Dicks, Matthew & Paskaramoorthy, Andrew & Gebbie, Tim, 2024. "A simple learning agent interacting with an agent-based market model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    3. Aaron Wheeler & Jeffrey D. Varner, 2023. "Scalable Agent-Based Modeling for Complex Financial Market Simulations," Papers 2312.14903, arXiv.org, revised Jan 2024.
    4. Dominic Bauer & Derick Diana & Tim Gebbie, 2024. "Correlation emergence in two coupled simulated limit order books," Papers 2408.03181, arXiv.org.
    5. Derick Diana & Tim Gebbie, 2023. "Anomalous diffusion and price impact in the fluid-limit of an order book," Papers 2310.06079, arXiv.org, revised Aug 2024.

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