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

TL;DR

This paper introduces a neural network-based framework for forecasting the term structure of crude oil futures prices, demonstrating superior accuracy over traditional models across various maturities and economic conditions.

Contribution

It develops a novel neural network approach integrated with the Nelson-Siegel model to improve crude oil futures price forecasts.

Findings

Neural network forecasts outperform benchmark models.

Forecast accuracy is consistent across different maturities.

The method is effective during recession and crisis periods.

Abstract

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.