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dc.contributor.authorUDDIN, Gazi Salah
dc.contributor.authorGENCAY, Ramazan
dc.contributor.authorBEKIROS, Stelios D.
dc.contributor.authorSAHAMKHADAM, Maziar
dc.date.accessioned2020-02-10T16:09:12Z
dc.date.available2020-02-10T16:09:12Z
dc.date.issued2019
dc.identifier.citationEconomics letters, 2019, Vol. 182, pp. 50-54en
dc.identifier.issn0165-1765
dc.identifier.issn1873-7374
dc.identifier.urihttps://hdl.handle.net/1814/66123
dc.descriptionAvailable online 8 June 2019en
dc.description.abstractWe 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.en
dc.description.sponsorshipJan Wallander Foundationen
dc.description.sponsorshipTom Hedelius Foundationen
dc.description.sponsorshipSiamon Foundationen
dc.language.isoen
dc.publisherElsevier Science Saen
dc.relation.ispartofEconomics lettersen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.subjectWavelet decompositionen
dc.subjectForecastingen
dc.subjectCrude oilen
dc.titleEnhancing the predictability of crude oil markets with hybrid wavelet approachesen
dc.typeArticle
dc.identifier.doi10.1016/j.econlet.2019.05.041
dc.identifier.volume182
dc.identifier.startpage50
dc.identifier.endpage54
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