Forecasting the term structure of crude oil futures prices with neural networks

The paper contributes to the limited literature modelling the term structure of crude oil markets. We explain the term structure of crude oil prices using the dynamic Nelson-Siegel model and propose to forecast oil prices using a generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month-, 3-month-, 6-month- and 12-month-ahead forecasts obtained from a focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity.