A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures |
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Authors: | Germán G Creamer Chihoon Lee |
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Institution: | 1. School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA;2. Visiting Scholar at Stern School of Business, New York University, New York, NY, USAgcreamer@stevens.eduhttps://orcid.org/0000-0002-3159-5153;4. School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USAhttps://orcid.org/0000-0001-5448-2787 |
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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. |
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Keywords: | Financial forecasting Lead-lag relationship Nonlinear correlation Energy finance Support vector machine Brownian partial distance correlation Random forests |
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