Some stylized facts of the Bitcoin market

Autores
Bariviera, Aurelio F.; Basgalla, María José; Hasperué, Waldo; Naiouf, Marcelo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión enviada
Descripción
In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compares Bitcoin and standard currencies dynamics and focuses on the analysis of returns at different time scales. We test the presence of long memory in return time series from 2011 to 2017, using transaction data from one Bitcoin platform. We compute the Hurst exponent by means of the Detrended Fluctuation Analysis method, using a sliding window in order to measure long range dependence. We detect that Hurst exponents changes significantly during the first years of existence of Bitcoin, tending to stabilize in recent times. Additionally, multiscale analysis shows a similar behavior of the Hurst exponent, implying a self-similar process.

Materia
Ingenierías y Tecnologías
Bitcoin
Hurst
DFA
Long memory
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/8578
Acceder
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oai_identifier_str oai:digital.cic.gba.gob.ar:11746/8578
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling Some stylized facts of the Bitcoin marketBariviera, Aurelio F.Basgalla, María JoséHasperué, WaldoNaiouf, MarceloIngenierías y TecnologíasBitcoinHurstDFALong memoryIn recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compares Bitcoin and standard currencies dynamics and focuses on the analysis of returns at different time scales. We test the presence of long memory in return time series from 2011 to 2017, using transaction data from one Bitcoin platform. We compute the Hurst exponent by means of the Detrended Fluctuation Analysis method, using a sliding window in order to measure long range dependence. We detect that Hurst exponents changes significantly during the first years of existence of Bitcoin, tending to stabilize in recent times. Additionally, multiscale analysis shows a similar behavior of the Hurst exponent, implying a self-similar process. <ul style= margin-left:0px; margin-right:0px > </ul>2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/8578enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:39:52Zoai:digital.cic.gba.gob.ar:11746/8578Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:39:52.913CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Some stylized facts of the Bitcoin market
title Some stylized facts of the Bitcoin market
spellingShingle Some stylized facts of the Bitcoin market
Bariviera, Aurelio F.
Ingenierías y Tecnologías
Bitcoin
Hurst
DFA
Long memory
title_short Some stylized facts of the Bitcoin market
title_full Some stylized facts of the Bitcoin market
title_fullStr Some stylized facts of the Bitcoin market
title_full_unstemmed Some stylized facts of the Bitcoin market
title_sort Some stylized facts of the Bitcoin market
dc.creator.none.fl_str_mv Bariviera, Aurelio F.
Basgalla, María José
Hasperué, Waldo
Naiouf, Marcelo
author Bariviera, Aurelio F.
author_facet Bariviera, Aurelio F.
Basgalla, María José
Hasperué, Waldo
Naiouf, Marcelo
author_role author
author2 Basgalla, María José
Hasperué, Waldo
Naiouf, Marcelo
author2_role author
author
author
dc.subject.none.fl_str_mv Ingenierías y Tecnologías
Bitcoin
Hurst
DFA
Long memory
topic Ingenierías y Tecnologías
Bitcoin
Hurst
DFA
Long memory
dc.description.none.fl_txt_mv In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compares Bitcoin and standard currencies dynamics and focuses on the analysis of returns at different time scales. We test the presence of long memory in return time series from 2011 to 2017, using transaction data from one Bitcoin platform. We compute the Hurst exponent by means of the Detrended Fluctuation Analysis method, using a sliding window in order to measure long range dependence. We detect that Hurst exponents changes significantly during the first years of existence of Bitcoin, tending to stabilize in recent times. Additionally, multiscale analysis shows a similar behavior of the Hurst exponent, implying a self-similar process. <ul style= margin-left:0px; margin-right:0px > </ul>
description In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compares Bitcoin and standard currencies dynamics and focuses on the analysis of returns at different time scales. We test the presence of long memory in return time series from 2011 to 2017, using transaction data from one Bitcoin platform. We compute the Hurst exponent by means of the Detrended Fluctuation Analysis method, using a sliding window in order to measure long range dependence. We detect that Hurst exponents changes significantly during the first years of existence of Bitcoin, tending to stabilize in recent times. Additionally, multiscale analysis shows a similar behavior of the Hurst exponent, implying a self-similar process. <ul style= margin-left:0px; margin-right:0px > </ul>
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/8578
url https://digital.cic.gba.gob.ar/handle/11746/8578
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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score 13.069144

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