Detecting speculative bubbles in metal prices: Evidence from GSADF test and machine learning approaches

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dc.authorid0000-0001-5738-690Xen_US
dc.authorscopusid16242962400en_US
dc.authorwosidB-1891-2010en_US
dc.contributor.authorÖzgur, Önder
dc.contributor.authorYılancı, Veli
dc.contributor.authorÖzbuğday, Fatih Cemil
dc.date.accessioned2023-04-07T06:23:56Z
dc.date.available2023-04-07T06:23:56Z
dc.date.issued2021en_US
dc.departmentFakülteler, Siyasal Bilgiler Fakültesi, İktisat Bölümü
dc.description.abstractThe importance of metal prices to real economic activity and financial markets has increased the focus on detecting price bubbles in precious and industrial metals. Several studies looked at the influence of macroeco- nomic factors in the formation of a single metal bubble and tried to identify bubble dates. Our study extends the literature and analyzes monthly gold, platinum, palladium, rhodium, silver, and aluminum, copper, lead, nickel, steel, tin prices over 1980M1-2019M12, and contributes to the literature in two ways: First, the analysis in- corporates the Generalized Supremum Augmented Dickey-Fuller (GSADF) test to detect potential bubbles. Sec- ond, the study evaluates the impact of potential financial, real, and speculative factors in the likelihood of price bubbles using the random forest method. Our findings indicate that financial factors are more critical in pre- dicting precious metal price bubbles. The monetary policy rate and the production index are important to predict bubbles in industrial metal prices. However, our findings suggest that speculative activity may not adequately predict metal price bubbles.en_US
dc.identifier.citationOzgur, O., Yilanci, V., & Ozbugday, F. C. (2021). Detecting speculative bubbles in metal prices: Evidence from GSADF test and machine learning approaches. Resources Policy, 74 doi:10.1016/j.resourpol.2021.102306en_US
dc.identifier.doi10.1016/j.resourpol.2021.102306
dc.identifier.issn0301-4207
dc.identifier.issn1873-7641
dc.identifier.scopus2-s2.0-85113328632
dc.identifier.urihttps://doi.org/10.1016/j.resourpol.2021.102306
dc.identifier.urihttps://hdl.handle.net/20.500.12428/3942
dc.identifier.volume74en_US
dc.identifier.wosWOS:000700368100070
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorYılancı, Veli
dc.language.isoen
dc.publisherElsevieren_US
dc.relation.ispartofResources Policyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMetal pricesen_US
dc.subjectMultiple bubblesen_US
dc.subjectRandom forest algorithmen_US
dc.subjectMacroeconomic factorsen_US
dc.titleDetecting speculative bubbles in metal prices: Evidence from GSADF test and machine learning approaches
dc.typeArticle

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