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

Jonas Hallgren and Timo Koski

Papers from arXiv.org

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.

Date: 2016-01
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-mst and nep-net
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1601.06651

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