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 random forest for regression, the forecast of energy futures improve when compared to a forecast based only on Granger causality.
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