期刊
APPLIED ENERGY
卷 164, 期 -, 页码 366-379出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2015.11.051
关键词
Term structure; Nelson-Siegel model; Dynamic neural networks; Crude oil futures
资金
- Czech Science Foundation [P402/12/G097]
- People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme FP7/under REA grant [609642]
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. (C) 2015 Elsevier Ltd. All rights reserved.
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