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

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  • Baruník, Jozef
  • Malinská, Barbora

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.

Suggested Citation

  • Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
  • Handle: RePEc:eee:appene:v:164:y:2016:i:c:p:366-379
    DOI: 10.1016/j.apenergy.2015.11.051
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    Cited by:

    1. Fanelli, Viviana & Maddalena, Lucia & Musti, Silvana, 2016. "Modelling electricity futures prices using seasonal path-dependent volatility," Applied Energy, Elsevier, vol. 173(C), pages 92-102.
    2. repec:gam:jeners:v:12:y:2019:i:19:p:3603-:d:269322 is not listed on IDEAS
    3. repec:gam:jeners:v:11:y:2018:i:12:p:3486-:d:190416 is not listed on IDEAS
    4. repec:eee:tefoso:v:126:y:2018:i:c:p:271-283 is not listed on IDEAS
    5. Gao, Xiangyun & Fang, Wei & An, Feng & Wang, Yue, 2017. "Detecting method for crude oil price fluctuation mechanism under different periodic time series," Applied Energy, Elsevier, vol. 192(C), pages 201-212.
    6. repec:eco:journ2:2018-03-37 is not listed on IDEAS
    7. repec:eee:appene:v:228:y:2018:i:c:p:2387-2397 is not listed on IDEAS
    8. repec:eee:intfor:v:34:y:2018:i:4:p:665-677 is not listed on IDEAS

    More about this item

    Keywords

    Term structure; Nelson–Siegel model; Dynamic neural networks; Crude oil futures;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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