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The role of volume in order book dynamics: a multivariate Hawkes process analysis

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  • Marcello Rambaldi
  • Emmanuel Bacry
  • Fabrizio Lillo

Abstract

We show that multivariate Hawkes processes coupled with the nonparametric estimation procedure first proposed in Bacry and Muzy (2015) can be successfully used to study complex interactions between the time of arrival of orders and their size, observed in a limit order book market. We apply this methodology to high-frequency order book data of futures traded at EUREX. Specifically, we demonstrate how this approach is amenable not only to analyze interplay between different order types (market orders, limit orders, cancellations) but also to include other relevant quantities, such as the order size, into the analysis, showing also that simple models assuming the independence between volume and time are not suitable to describe the data.

Suggested Citation

  • Marcello Rambaldi & Emmanuel Bacry & Fabrizio Lillo, 2016. "The role of volume in order book dynamics: a multivariate Hawkes process analysis," Papers 1602.07663, arXiv.org.
    • Handle: RePEc:arx:papers:1602.07663
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    References listed on IDEAS

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    10. V. Filimonov & D. Sornette, 2015. "Apparent criticality and calibration issues in the Hawkes self-excited point process model: application to high-frequency financial data," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1293-1314, August.
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    Cited by:

    1. Xiaofei Lu & Frédéric Abergel, 2017. "Limit order book modelling with high dimensional Hawkes processes," Working Papers hal-01512430, HAL.
    2. Federico Gonzalez & Mark Schervish, 2017. "Instantaneous order impact and high-frequency strategy optimization in limit order books," Papers 1707.01167, arXiv.org, revised Oct 2017.
    3. Nino Antulov-Fantulin & Tian Guo & Fabrizio Lillo, 2020. "Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets," Papers 2005.09356, arXiv.org, revised Dec 2020.
    4. Massil Achab & Emmanuel Bacry & Jean-Franc{c}ois Muzy & Marcello Rambaldi, 2017. "Analysis of order book flows using a nonparametric estimation of the branching ratio matrix," Papers 1706.03411, arXiv.org.
    5. Nino Antulov-Fantulin & Tian Guo & Fabrizio Lillo, 2021. "Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 905-940, December.
    6. Maxime Morariu-Patrichi & Mikko S. Pakkanen, 2017. "Hybrid marked point processes: characterisation, existence and uniqueness," Papers 1707.06970, arXiv.org, revised Oct 2018.
    7. Achraf Bahamou & Maud Doumergue & Philippe Donnat, 2019. "Hawkes processes for credit indices time series analysis: How random are trades arrival times?," Papers 1902.03714, arXiv.org.
    8. Xiaofei Lu & Frédéric Abergel, 2018. "High dimensional Hawkes processes for limit order books Modelling, empirical analysis and numerical calibration," Post-Print hal-01686122, HAL.

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