Nonlinear Forecasting of Energy Futures

Germán G. Creamer

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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.

Original languageEnglish
Title of host publicationStudies in Big Data
Pages3-14
Number of pages12
DOIs
StatePublished - 2019

Publication series

NameStudies in Big Data
Volume40
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

Keywords

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

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