Non-Linear Forecasting of Energy Futures

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 prices of oil, coal, and natural gas. When these linear and non-linear relationships are used to forecast energy futures with a non-linear regression 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

JEL Classification: G13, Q41, C53, C32