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Simulation and estimation of a point-process market-model with a matching engine

Author

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  • Ivan Jericevich
  • Patrick Chang
  • Tim Gebbie

Abstract

The extent to which a matching engine can cloud the modelling of underlying order submission and management processes in a financial market remains an unanswered concern with regards to market models. Here we consider a 10-variate Hawkes process with simple rules to simulate common order types which are submitted to a matching engine. Hawkes processes can be used to model the time and order of events, and how these events relate to each other. However, they provide a freedom with regards to implementation mechanics relating to the prices and volumes of injected orders. This allows us to consider a reference Hawkes model and two additional models which have rules that change the behaviour of limit orders. The resulting trade and quote data from the simulations are then calibrated and compared with the original order generating process to determine the extent with which implementation rules can distort model parameters. Evidence from validation and hypothesis tests suggest that the true model specification can be significantly distorted by market mechanics, and that practical considerations not directly due to model specification can be important with regards to model identification within an inherently asynchronous trading environment.

Suggested Citation

  • Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of a point-process market-model with a matching engine," Papers 2105.02211, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2105.02211
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Derick Diana & Tim Gebbie, 2023. "Anomalous diffusion and price impact in the fluid-limit of an order book," Papers 2310.06079, arXiv.org, revised Jan 2024.
    3. Matthew Dicks & Andrew Paskaramoorthy & Tim Gebbie, 2023. "Many learning agents interacting with an agent-based market model," Papers 2303.07393, arXiv.org, revised Nov 2023.

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