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

01/25/2016

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by   Jonas Hallgren, et al.

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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.