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arXiv · 1601.06651

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

Jonas Hallgren·Timo Koski
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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. 2016-01-25. Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data. https://arxiv.org/abs/1601.06651

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