标题： 用神经网络预测原油期货价格的期限结构 显示英文标题

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

摘要： 本文对建模原油市场期限结构的稀有文献做出了贡献。 我们使用动态Nelson-Siegel模型解释原油价格的期限结构，并提出基于神经网络的广义回归框架来预测它们。 新提出的框架在涵盖多个重要衰退和危机时期的24年原油期货价格上进行了实证测试。 我们发现，从聚焦时滞神经网络获得的1个月、3个月、6个月和12个月前瞻预测比其他基准模型的预测要显著准确得多。 所提出的预测策略在所有到期时间上都产生了最低的误差。 显示英文摘要

摘要： 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.