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

Autor:	Hallgren, Jonas, Koski, Timo
Rok vydání:	2016
Předmět:	Statistics - Machine Learning Quantitative Finance - Trading and Market Microstructure 60J28 (Primary) 65S99 (Secondary)
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1601.06651 Zobrazit plný text záznamu View this record from Arxiv