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

By Jonas Hallgren, Timo Koski. 2016

4 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Juho Kanniainen
2. Moncef Gabbouj
3. Dat Thanh Tran
4. Alexandros Iosifidis
5. Martin Magris

In Computer Networks and Communications

1. Juho Kanniainen
2. Alexandros Iosifidis
3. Martin Magris
4. Moncef Gabbouj

Citations in the corpus

• Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “All kinds of deep architectures have been proposed, ranging from traditional multilayer feed-forward models [6], [7], [8] to Convolutional Neural Network (CNN) [9], Recurrent Neural Network (RNN) [10], [11], [12], Deep Belief Networks [13], [14], [15].”
• Benchmark Dataset for Mid-Price Prediction of Limit Order Book data (2017, methodology): “Hallgren and Koskik [22] use Continuous Time Bayesian Networks (CTBNs) for causality detection.”