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

Jozef Baruník and Barbora Malinska

Papers from arXiv.org

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.

Date: 2015-04
New Economics Papers: this item is included in nep-cmp, nep-ene and nep-for
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Citations: View citations in EconPapers (1)

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http://arxiv.org/pdf/1504.04819 Latest version (application/pdf)

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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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1504.04819

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