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

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Τίτλος: Forecasting the term structure of crude oil futures prices with neural networks.
Συγγραφείς: Baruník, Jozef1,2 barunik@utia.cas.cz, Malinská, Barbora1
Πηγή: Applied Energy. Feb2016, Vol. 164, p366-379. 14p.
Θεματικοί όροι: *Power resources forecasting, *Petroleum sales & prices, *Futures, *Artificial neural networks, *Time delay systems, *Regression analysis
Περίληψη: 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 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 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. [ABSTRACT FROM AUTHOR]
Βάση Δεδομένων: Academic Search Index
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  – Url: https://www.doi.org/10.1016/j.apenergy.2015.11.051?
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  Data: Forecasting the term structure of crude oil futures prices with neural networks.
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  Data: <searchLink fieldCode="AR" term="%22Baruník%2C+Jozef%22">Baruník, Jozef</searchLink><relatesTo>1,2</relatesTo><i> barunik@utia.cas.cz</i><br /><searchLink fieldCode="AR" term="%22Malinská%2C+Barbora%22">Malinská, Barbora</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Applied+Energy%22">Applied Energy</searchLink>. Feb2016, Vol. 164, p366-379. 14p.
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  Data: *<searchLink fieldCode="DE" term="%22Power+resources+forecasting%22">Power resources forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Petroleum+sales+%26+prices%22">Petroleum sales & prices</searchLink><br />*<searchLink fieldCode="DE" term="%22Futures%22">Futures</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Time+delay+systems%22">Time delay systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink>
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  Data: 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 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 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. [ABSTRACT FROM AUTHOR]
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        Value: 10.1016/j.apenergy.2015.11.051
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 366
    Subjects:
      – SubjectFull: Power resources forecasting
        Type: general
      – SubjectFull: Petroleum sales & prices
        Type: general
      – SubjectFull: Futures
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Time delay systems
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      – SubjectFull: Regression analysis
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      – TitleFull: Forecasting the term structure of crude oil futures prices with neural networks.
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              Text: Feb2016
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              Y: 2016
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