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Long Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple

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dc.contributor.authorOKUR, MUSTAFA
dc.contributor.authorsSoylu, Pinar Kaya; Okur, Mustafa; Catikkas, Ozgur; Altintig, Z. Ayca
dc.date.accessioned2022-03-14T10:53:45Z
dc.date.available2022-03-14T10:53:45Z
dc.date.issued2020-05-29
dc.description.abstractThis paper examines the volatility of cryptocurrencies, with particular attention to their potential long memory properties. Using daily data for the three major cryptocurrencies, namely Ripple, Ethereum, and Bitcoin, we test for the long memory property using, Rescaled Range Statistics (R/S), Gaussian Semi Parametric (GSP) and the Geweke and Porter-Hudak (GPH) Model Method. Our findings show that squared returns of three cryptocurrencies have a significant long memory, supporting the use of fractional Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) extensions as suitable modelling technique. Our findings indicate that the Hyperbolic GARCH (HYGARCH) model appears to be the best fitted model for Bitcoin. On the other hand, the Fractional Integrated GARCH (FIGARCH) model with skewed student distribution produces better estimations for Ethereum. Finally, FIGARCH model with student distribution appears to give a good fit for Ripple return. Based on Kupieck's tests for Value at Risk (VaR) back-testing and expected shortfalls we can conclude that our models perform correctly in most of the cases for both the negative and positive returns.
dc.identifier.doi10.3390/jrfm13060107
dc.identifier.eissn1911-8074
dc.identifier.issn1911-8066
dc.identifier.urihttps://hdl.handle.net/11424/245357
dc.identifier.wosWOS:000551232500006
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofJOURNAL OF RISK AND FINANCIAL MANAGEMENT
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectvolatility modelling
dc.subjectcryptocurrency
dc.subjectvalue at risk
dc.subjectexpected shortfall
dc.subjectlong memory
dc.subjectCONDITIONAL HETEROSCEDASTICITY
dc.subjectMARKETS
dc.subjectPERSISTENCE
dc.subjectRETURN
dc.subjectPRICE
dc.titleLong Memory in the Volatility of Selected Cryptocurrencies: Bitcoin, Ethereum and Ripple
dc.typearticle
dspace.entity.typePublication
local.avesis.id99e78573-fab5-4f70-91e2-34d48fe3918e
local.import.packageSS16
local.indexed.atWOS
local.journal.articlenumber107
local.journal.numberofpages20
oaire.citation.issue6
oaire.citation.titleJOURNAL OF RISK AND FINANCIAL MANAGEMENT
oaire.citation.volume13
relation.isAuthorOfPublication9c312e36-7ac0-4a64-8b07-306fe1c1a3b3
relation.isAuthorOfPublication.latestForDiscovery9c312e36-7ac0-4a64-8b07-306fe1c1a3b3

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