英文标题：
《Forecasting the term structure of crude oil futures prices with neural
networks》
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作者：
Jozef Barunik and Barbora Malinska
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最新提交年份：
2015
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英文摘要：
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.
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中文摘要：
这篇论文有助于罕见的文献对原油市场的期限结构进行建模。我们用动态Nelson-Siegel模型解释了原油价格的期限结构，并提出用基于神经网络的广义回归框架对其进行预测。新提出的框架在涵盖几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检验。我们发现，从聚焦时滞神经网络获得的提前1个月、3个月、6个月和12个月的预测比其他基准模型的预测要准确得多。所提出的预测策略在整个成熟期内产生的误差最校
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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