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Enhancing the predictability of crude oil markets with hybrid wavelet approaches
Linköping University, Sweden.
Simon Fraser Univ, Canada.
European University Institute, Italy;Athens University of Economics and Business, Greece.
Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics.
2019 (English)In: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 182, p. 50-54Article in journal (Refereed) Published
Abstract [en]

We explore the robustness, efficiency and accuracy of the multi-scale forecasting in crude oil markets. We adopt a novel hybrid wavelet auto-ARMA model to detect the inherent nonlinear dynamics of crude oil returns with an explicitly defined hierarchical structure. Entropic estimation is employed to calculate the optimal level of the decomposition. The wavelet-based forecasting method accounts for the chaotic behavior of oil series, whilst captures drifts, spikes and other non-stationary effects which common frequency-domain methods miss out completely. These results shed new light upon the predictability of crude oil markets in nonstationary settings. (C) 2019 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 182, p. 50-54
Keywords [en]
Wavelet decomposition, Forecasting, Crude oil
National Category
Economics and Business
Research subject
Economy
Identifiers
URN: urn:nbn:se:lnu:diva-89244DOI: 10.1016/j.econlet.2019.05.041ISI: 000481724400013OAI: oai:DiVA.org:lnu-89244DiVA, id: diva2:1354092
Available from: 2019-09-24 Created: 2019-09-24 Last updated: 2019-09-24Bibliographically approved

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Sahamkhadam, Maziar

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  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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