Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data
This work addresses causality modeling in high-frequency finance, but it appears incremental as it builds on existing continuous time Bayesian network methods.
The authors tackled the problem of expressing causality in continuous time Bayesian networks for high-frequency financial data, presenting a framework with a new causality measure that showed improved performance over older models when calibrated to market data.
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.