Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks

26 Pages Posted: 20 Apr 2015

See all articles by Jozef Baruník

Jozef Baruník

Charles University in Prague - Department of Economics; Institute of Information Theory and Automation, Prague

Barbora Malinská

Charles University in Prague - Faculty of Social Sciences

Date Written: April 19, 2015

Abstract

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.

Keywords: term structure, Nelson-Siegel model, dynamic neural networks, crude oil futures

JEL Classification: C14, C32, C45, G02, G17

Suggested Citation

Barunik, Jozef and Malinská, Barbora, Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks (April 19, 2015). Available at SSRN: https://ssrn.com/abstract=2596175 or http://dx.doi.org/10.2139/ssrn.2596175

Jozef Barunik (Contact Author)

Charles University in Prague - Department of Economics ( email )

Opletalova 26
Prague 1, 110 00
Czech Republic

HOME PAGE: http://ies.fsv.cuni.cz/en/staff/barunik

Institute of Information Theory and Automation, Prague ( email )

Pod vodarenskou vezi 4
CZ-18208 Praha 8
Czech Republic

HOME PAGE: http://staff.utia.cas.cz/barunik/home.htm

Barbora Malinská

Charles University in Prague - Faculty of Social Sciences ( email )

Opletalova St. 26
Prague, 11000
Czech Republic

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