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

Result code in IS VaVaI

RIV/67985556:_____/16:00453168 - isvavai.cz

Alternative codes found

RIV/00216208:11230/16:10317566

Result on the web

http://dx.doi.org/10.1016/j.apenergy.2015.11.051

DOI - Digital Object Identifier

10.1016/j.apenergy.2015.11.051

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

—

Czech description

—

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

AH - Economics

OECD FORD branch

—

Project

GBP402/12/G097: DYME-Dynamic Models in Economics

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

Name of the periodical

Applied Energy

ISSN

0306-2619

e-ISSN

—

Volume of the periodical

164

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

14

Pages from-to

366-379

UT code for WoS article

000372379700035

EID of the result in the Scopus database

2-s2.0-84951017088