A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020

After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important c...
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Autor*in:	Mendes, Beatriz Vaz de Melo [verfasserIn] Fluminense Carneiro, André [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Übergeordnetes Werk:	Enthalten in: Journal of risk and financial management - Basel : MDPI, 2008, 13(2020), 9/192 vom: Sept., Seite 1-21
Übergeordnetes Werk:	volume:13 ; year:2020 ; number:9/192 ; month:09 ; pages:1-21

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.3390/jrfm13090192

Katalog-ID:	1743098405

Internformat


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allfields	10.3390/jrfm13090192 doi 10419/239302 hdl (DE-627)1743098405 (DE-599)KXP1743098405 DE-627 ger DE-627 rda eng C1 C3 C4 C5 G0 G1 jelc Mendes, Beatriz Vaz de Melo verfasserin (DE-588)133245535 (DE-627)538719982 (DE-576)29972221X aut A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020 Beatriz Vaz de Melo Mendes and André Fluminense Carneiro 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Fluminense Carneiro, André verfasserin aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 13(2020), 9/192 vom: Sept., Seite 1-21 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:13 year:2020 number:9/192 month:09 pages:1-21 https://www.mdpi.com/1911-8074/13/9/192/pdf Verlag kostenfrei https://doi.org/10.3390/jrfm13090192 Resolving-System kostenfrei http://hdl.handle.net/10419/239302 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 13 2020 9/192 9 1-21 26 01 0206 3826472500 x1z 17-12-20 2403 01 DE-LFER 3832534199 00 --%%-- --%%-- n --%%-- l01 08-01-21 2403 01 DE-LFER https://doi.org/10.3390/jrfm13090192 2403 01 DE-LFER https://www.mdpi.com/1911-8074/13/9/192/pdf 26 00 DE-206 56 COVID-19 26 00 DE-206 56 Bitcoin 26 00 DE-206 56 cointegrated VAR 26 00 DE-206 56 crypto-currency 26 00 DE-206 56 EVT 26 00 DE-206 56 pair-copulas 26 00 DE-206 56 risk measures
spelling	10.3390/jrfm13090192 doi 10419/239302 hdl (DE-627)1743098405 (DE-599)KXP1743098405 DE-627 ger DE-627 rda eng C1 C3 C4 C5 G0 G1 jelc Mendes, Beatriz Vaz de Melo verfasserin (DE-588)133245535 (DE-627)538719982 (DE-576)29972221X aut A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020 Beatriz Vaz de Melo Mendes and André Fluminense Carneiro 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Fluminense Carneiro, André verfasserin aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 13(2020), 9/192 vom: Sept., Seite 1-21 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:13 year:2020 number:9/192 month:09 pages:1-21 https://www.mdpi.com/1911-8074/13/9/192/pdf Verlag kostenfrei https://doi.org/10.3390/jrfm13090192 Resolving-System kostenfrei http://hdl.handle.net/10419/239302 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 13 2020 9/192 9 1-21 26 01 0206 3826472500 x1z 17-12-20 2403 01 DE-LFER 3832534199 00 --%%-- --%%-- n --%%-- l01 08-01-21 2403 01 DE-LFER https://doi.org/10.3390/jrfm13090192 2403 01 DE-LFER https://www.mdpi.com/1911-8074/13/9/192/pdf 26 00 DE-206 56 COVID-19 26 00 DE-206 56 Bitcoin 26 00 DE-206 56 cointegrated VAR 26 00 DE-206 56 crypto-currency 26 00 DE-206 56 EVT 26 00 DE-206 56 pair-copulas 26 00 DE-206 56 risk measures
allfields_unstemmed	10.3390/jrfm13090192 doi 10419/239302 hdl (DE-627)1743098405 (DE-599)KXP1743098405 DE-627 ger DE-627 rda eng C1 C3 C4 C5 G0 G1 jelc Mendes, Beatriz Vaz de Melo verfasserin (DE-588)133245535 (DE-627)538719982 (DE-576)29972221X aut A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020 Beatriz Vaz de Melo Mendes and André Fluminense Carneiro 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Fluminense Carneiro, André verfasserin aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 13(2020), 9/192 vom: Sept., Seite 1-21 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:13 year:2020 number:9/192 month:09 pages:1-21 https://www.mdpi.com/1911-8074/13/9/192/pdf Verlag kostenfrei https://doi.org/10.3390/jrfm13090192 Resolving-System kostenfrei http://hdl.handle.net/10419/239302 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 13 2020 9/192 9 1-21 26 01 0206 3826472500 x1z 17-12-20 2403 01 DE-LFER 3832534199 00 --%%-- --%%-- n --%%-- l01 08-01-21 2403 01 DE-LFER https://doi.org/10.3390/jrfm13090192 2403 01 DE-LFER https://www.mdpi.com/1911-8074/13/9/192/pdf 26 00 DE-206 56 COVID-19 26 00 DE-206 56 Bitcoin 26 00 DE-206 56 cointegrated VAR 26 00 DE-206 56 crypto-currency 26 00 DE-206 56 EVT 26 00 DE-206 56 pair-copulas 26 00 DE-206 56 risk measures
allfieldsGer	10.3390/jrfm13090192 doi 10419/239302 hdl (DE-627)1743098405 (DE-599)KXP1743098405 DE-627 ger DE-627 rda eng C1 C3 C4 C5 G0 G1 jelc Mendes, Beatriz Vaz de Melo verfasserin (DE-588)133245535 (DE-627)538719982 (DE-576)29972221X aut A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020 Beatriz Vaz de Melo Mendes and André Fluminense Carneiro 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Fluminense Carneiro, André verfasserin aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 13(2020), 9/192 vom: Sept., Seite 1-21 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:13 year:2020 number:9/192 month:09 pages:1-21 https://www.mdpi.com/1911-8074/13/9/192/pdf Verlag kostenfrei https://doi.org/10.3390/jrfm13090192 Resolving-System kostenfrei http://hdl.handle.net/10419/239302 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 13 2020 9/192 9 1-21 26 01 0206 3826472500 x1z 17-12-20 2403 01 DE-LFER 3832534199 00 --%%-- --%%-- n --%%-- l01 08-01-21 2403 01 DE-LFER https://doi.org/10.3390/jrfm13090192 2403 01 DE-LFER https://www.mdpi.com/1911-8074/13/9/192/pdf 26 00 DE-206 56 COVID-19 26 00 DE-206 56 Bitcoin 26 00 DE-206 56 cointegrated VAR 26 00 DE-206 56 crypto-currency 26 00 DE-206 56 EVT 26 00 DE-206 56 pair-copulas 26 00 DE-206 56 risk measures
allfieldsSound	10.3390/jrfm13090192 doi 10419/239302 hdl (DE-627)1743098405 (DE-599)KXP1743098405 DE-627 ger DE-627 rda eng C1 C3 C4 C5 G0 G1 jelc Mendes, Beatriz Vaz de Melo verfasserin (DE-588)133245535 (DE-627)538719982 (DE-576)29972221X aut A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020 Beatriz Vaz de Melo Mendes and André Fluminense Carneiro 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Fluminense Carneiro, André verfasserin aut Enthalten in Journal of risk and financial management Basel : MDPI, 2008 13(2020), 9/192 vom: Sept., Seite 1-21 Online-Ressource (DE-627)770970427 (DE-600)2739117-6 (DE-576)395129494 1911-8074 nnns volume:13 year:2020 number:9/192 month:09 pages:1-21 https://www.mdpi.com/1911-8074/13/9/192/pdf Verlag kostenfrei https://doi.org/10.3390/jrfm13090192 Resolving-System kostenfrei http://hdl.handle.net/10419/239302 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_90 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2026 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 13 2020 9/192 9 1-21 26 01 0206 3826472500 x1z 17-12-20 2403 01 DE-LFER 3832534199 00 --%%-- --%%-- n --%%-- l01 08-01-21 2403 01 DE-LFER https://doi.org/10.3390/jrfm13090192 2403 01 DE-LFER https://www.mdpi.com/1911-8074/13/9/192/pdf 26 00 DE-206 56 COVID-19 26 00 DE-206 56 Bitcoin 26 00 DE-206 56 cointegrated VAR 26 00 DE-206 56 crypto-currency 26 00 DE-206 56 EVT 26 00 DE-206 56 pair-copulas 26 00 DE-206 56 risk measures
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source	Enthalten in Journal of risk and financial management 13(2020), 9/192 vom: Sept., Seite 1-21 volume:13 year:2020 number:9/192 month:09 pages:1-21
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spellingShingle	Mendes, Beatriz Vaz de Melo jelc C1 26 COVID-19 26 Bitcoin 26 cointegrated VAR 26 crypto-currency 26 EVT 26 pair-copulas 26 risk measures A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020
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topic_title	C1 C3 C4 C5 G0 G1 jelc 26 00 DE-206 56 COVID-19 26 00 DE-206 56 Bitcoin 26 00 DE-206 56 cointegrated VAR 26 00 DE-206 56 crypto-currency 26 00 DE-206 56 EVT 26 00 DE-206 56 pair-copulas 26 00 DE-206 56 risk measures A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020 Beatriz Vaz de Melo Mendes and André Fluminense Carneiro
topic	jelc C1 26 COVID-19 26 Bitcoin 26 cointegrated VAR 26 crypto-currency 26 EVT 26 pair-copulas 26 risk measures
topic_unstemmed	jelc C1 26 COVID-19 26 Bitcoin 26 cointegrated VAR 26 crypto-currency 26 EVT 26 pair-copulas 26 risk measures
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title	A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020
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title_full	A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020 Beatriz Vaz de Melo Mendes and André Fluminense Carneiro
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title_sort	comprehensive statistical analysis of the six major crypto-currencies from august 2015 through june 2020
title_auth	A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020
abstract	After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data.
abstractGer	After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data.
abstract_unstemmed	After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. To complete the analysis we provide a brief discussion on the effects of the COVID-19 pandemic on the crypto-market by including the first semester of 2020 data.
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title_short	A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020
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up_date	2024-07-04T09:02:40.935Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1743098405</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20211025102212.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201217s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/jrfm13090192</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/239302</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1743098405</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1743098405</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C1</subfield><subfield code="a">C3</subfield><subfield code="a">C4</subfield><subfield code="a">C5</subfield><subfield code="a">G0</subfield><subfield code="a">G1</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mendes, Beatriz Vaz de Melo</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)133245535</subfield><subfield code="0">(DE-627)538719982</subfield><subfield code="0">(DE-576)29972221X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020</subfield><subfield code="c">Beatriz Vaz de Melo Mendes and André Fluminense Carneiro</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">After more than a decade of existence, crypto-currencies may now be considered an important class of assets presenting some unique appealing characteristics but also sharing some features with real financial assets. This paper provides a comprehensive statistical analysis of the six most important crypto-currencies from the period 2015-2020. Using daily data we (1) showed that the returns present many of the stylized facts often observed for stock assets, (2) modeled the returns underlying distribution using a semi-parametric mixture model based on the extreme value theory, (3) showed that the returns are weakly autocorrelated and confirmed the presence of long memory as well as short memory in the GARCH volatility, (4) used an econometric approach to compute risk measures, such as the value-at-risk, the expected shortfall, and drawups, (5) found that the crypto-coins´ price trajectories do not contain speculative bubbles and that they move together maintaining the long run equilibrium, and (6) using static and dynamic D-vine pair-copula models, assessed the true dependence structure among the crypto-assets, obtaining robust copula based bivariate dynamic measures of association. The analyses indicate that the strength of dependence among the crypto-currencies has increased over the recent years in the cointegrated crypto-market. The conclusions reached will help investors to manage risk while identifying opportunities for alternative diversified and profitable investments. 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A comprehensive statistical analysis of the six major crypto-currencies from August 2015 through June 2020

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