Nonlinear Forecasting of Energy Futures

Chapter
Part of the Studies in Big Data book series (SBD, volume 40)

Abstract

This paper proposes the use of the Brownian distance correlation for feature selection and for conducting a lead-lag analysis of energy time series. Brownian distance correlation determines relationships similar to those identified by the linear Granger causality test, and it also uncovers additional non-linear relationships among the log return of oil, coal, and natural gas. When these linear and non-linear relationships are used to forecast the direction of energy futures log return with a non-linear classification method such as support vector machine, the forecast of energy futures log return improve when compared to a forecast based only on Granger causality.

Keywords

Financial forecasting Lead-lag relationship Non-linear correlation Energy finance Support vector machine Artificial agents 

Notes

Acknowledgements

The author thanks participants of the Eastern Economics Association meeting 2014, the AAAI 2014 Fall Symposium on Energy Market Predictions, Dror Kennett, Alex Moreno, and three anonymous referees for their comments and suggestions. The author also thanks the Howe School Alliance for Technology Management for financial support provided to conduct this research. The opinions presented are the exclusive responsibility of the author.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of BusinessStevens Institute of TechnologyHobokenUSA

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