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Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data

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  • Jonas Hallgren
  • Timo Koski

Abstract

Continuous time Bayesian networks are investigated with a special focus on their ability to express causality. A framework is presented for doing inference in these networks. The central contributions are a representation of the intensity matrices for the networks and the introduction of a causality measure. A new model for high-frequency financial data is presented. It is calibrated to market data and by the new causality measure it performs better than older models.

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  • Jonas Hallgren & Timo Koski, 2016. "Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data," Papers 1601.06651, arXiv.org.
    • Handle: RePEc:arx:papers:1601.06651
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    References listed on IDEAS

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    7. Odd O. Aalen & Kjetil Røysland & Jon Michael Gran & Bruno Ledergerber, 2012. "Causality, mediation and time: a dynamic viewpoint," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(4), pages 831-861, October.
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    Cited by:

    1. Dat Thanh Tran & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Tensor Representation in High-Frequency Financial Data for Price Change Prediction," Papers 1709.01268, arXiv.org, revised Nov 2017.
    2. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods," Papers 1705.03233, arXiv.org, revised Mar 2020.

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