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

TL;DR

This paper develops a framework for causal inference in continuous time Bayesian networks, introduces a new causality measure, and presents a high-frequency financial data model that outperforms previous models.

Contribution

It provides a novel representation of intensity matrices and a causality measure, enhancing the modeling of high-frequency financial data.

Findings

The new causality measure effectively captures causal relationships.

The proposed model outperforms older models in market data calibration.

The framework enables better inference in continuous time Bayesian networks.

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.