  The paper contributes to the rare literature modeling term structure of crude
oil markets. We explain term structure of crude oil prices using dynamic
Nelson-Siegel model, and propose to forecast them with the 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 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.
