Nonlinear Forecasting of Energy Futures

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.