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A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures.

Authors :
Creamer, Germán G.
Lee, Chihoon
Source :
Quantitative Finance. Sep2019, Vol. 19 Issue 9, p1531-1542. 12p. 1 Diagram, 8 Charts, 3 Graphs.
Publication Year :
2019

Abstract

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14697688
Volume :
19
Issue :
9
Database :
Academic Search Index
Journal :
Quantitative Finance
Publication Type :
Academic Journal
Accession number :
137968566
Full Text :
https://doi.org/10.1080/14697688.2019.1622300