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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00453168" target="_blank" >RIV/67985556:_____/16:00453168 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11230/16:10317566
Result on the web
<a href="http://dx.doi.org/10.1016/j.apenergy.2015.11.051" target="_blank" >http://dx.doi.org/10.1016/j.apenergy.2015.11.051</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.apenergy.2015.11.051" target="_blank" >10.1016/j.apenergy.2015.11.051</a>

Result language
angličtina
Original language name
Forecasting the term structure of crude oil futures prices with neural networks
Original language description
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.
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
AH - Economics
OECD FORD branch
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Project
<a href="/en/project/GBP402%2F12%2FG097" target="_blank" >GBP402/12/G097: DYME-Dynamic Models in Economics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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