Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models

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dc.authoridBekun, Festus Victor/0000-0003-4948-6905
dc.contributor.authorYildirim, Hakan
dc.contributor.authorBekun, Festus Victor
dc.date.accessioned2024-09-11T19:52:30Z
dc.date.available2024-09-11T19:52:30Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractThe investment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013-03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.en_US
dc.description.sponsorshipThe Authors of this article also assures that they follow the springer publishing procedures and agree to publish it as any form of access article confirming to subscribe access standards and licensing.en_US
dc.description.sponsorshipAuthor gratitude is extended to the prospective editor(s) and reviewers that will/have spared time to guide toward a successful publication.r The Authors of this article also assures that they follow the springer publishing procedures and agree to publish it as any form of access article confirming to subscribe access standards and licensing.en_US
dc.identifier.doi10.1186/s43093-023-00255-8
dc.identifier.issn2314-7202
dc.identifier.issn2314-7210
dc.identifier.issue1en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1186/s43093-023-00255-8
dc.identifier.urihttps://hdl.handle.net/11363/7958
dc.identifier.volume9en_US
dc.identifier.wosWOS:001066479500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofFuture Business Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectBitcoin volumeen_US
dc.subjectVolatility returnsen_US
dc.subjectARMAen_US
dc.subjectARCHen_US
dc.subjectGARCHen_US
dc.subjectC22en_US
dc.subjectC32en_US
dc.subjectG15en_US
dc.titlePredicting volatility of bitcoin returns with ARCH, GARCH and EGARCH modelsen_US
dc.typeArticleen_US

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