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

Jozef Baruník and Barbora Malinská

Applied Energy, 2016, vol. 164, issue C, 366-379

Abstract: 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 24years 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.

Keywords: Term structure; Nelson–Siegel model; Dynamic neural networks; Crude oil futures (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (19)

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Related works:
Working Paper: Forecasting the term structure of crude oil futures prices with neural networks (2015)
Working Paper: Forecasting the Term Structure of Crude Oil Futures Prices with Neural Networks (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:164:y:2016:i:c:p:366-379

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DOI: 10.1016/j.apenergy.2015.11.051

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