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

Jozef Baruník and Barbora Malinska
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Barbora Malinska: Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Smetanovo nabrezi 6, 111 01 Prague 1, Czech Republic

No 2015/25, Working Papers IES from Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies

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 (search for similar items in EconPapers)
JEL-codes: C14 C32 C45 G02 G17 (search for similar items in EconPapers)
Pages: 31pages
Date: 2015-11, Revised 2015-11
New Economics Papers: this item is included in nep-cmp, nep-ene, nep-for and nep-ore
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Related works:
Journal Article: Forecasting the term structure of crude oil futures prices with neural networks (2016)
Working Paper: Forecasting the term structure of crude oil futures prices with neural networks (2015)
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