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State-dependent Hawkes processes and their application to limit order book modelling

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  • Maxime Morariu-Patrichi
  • Mikko S. Pakkanen

Abstract

We study statistical aspects of state-dependent Hawkes processes, which are an extension of Hawkes processes where a self- and cross-exciting counting process and a state process are fully coupled, interacting with each other. The excitation kernel of the counting process depends on the state process that, reciprocally, switches state when there is an event in the counting process. We first establish the existence and uniqueness of state-dependent Hawkes processes and explain how they can be simulated. Then we develop maximum likelihood estimation methodology for parametric specifications of the process. We apply state-dependent Hawkes processes to high-frequency limit order book data, allowing us to build a novel model that captures the feedback loop between the order flow and the shape of the limit order book. We estimate two specifications of the model, using the bid-ask spread and the queue imbalance as state variables, and find that excitation effects in the order flow are strongly state-dependent. Additionally, we find that the endogeneity of the order flow, measured by the magnitude of excitation, is also state-dependent, being more pronounced in disequilibrium states of the limit order book.

Suggested Citation

  • Maxime Morariu-Patrichi & Mikko S. Pakkanen, 2018. "State-dependent Hawkes processes and their application to limit order book modelling," Papers 1809.08060, arXiv.org, revised Sep 2021.
  • Handle: RePEc:arx:papers:1809.08060
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    References listed on IDEAS

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    4. Ioane Muni Toke & Nakahiro Yoshida, 2020. "Marked point processes and intensity ratios for limit order book modeling," Papers 2001.08442, arXiv.org.
    5. Dharmaraja Selvamuthu & Paola Tardelli, 2022. "Infinite-server systems with Hawkes arrivals and Hawkes services," Queueing Systems: Theory and Applications, Springer, vol. 101(3), pages 329-351, August.
    6. Zhicheng Li & Haipeng Xing, 2022. "High-Frequency Quote Volatility Measurement Using a Change-Point Intensity Model," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    7. Antoine Fosset & Jean-Philippe Bouchaud & Michael Benzaquen, 2020. "Endogenous Liquidity Crises," Working Papers hal-02567495, HAL.
    8. Antoine Fosset & Jean-Philippe Bouchaud & Michael Benzaquen, 2019. "Endogenous Liquidity Crises," Papers 1912.00359, arXiv.org, revised Feb 2020.
    9. Antoine Fosset & Jean-Philippe Bouchaud & Michael Benzaquen, 2020. "Non-parametric Estimation of Quadratic Hawkes Processes for Order Book Events," Papers 2005.05730, arXiv.org.
    10. Jonathan Sadighian, 2019. "Deep Reinforcement Learning in Cryptocurrency Market Making," Papers 1911.08647, arXiv.org.
    11. Jonathan Sadighian, 2020. "Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making," Papers 2004.06985, arXiv.org.

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