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DC Field | Value | Language |
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dc.contributor.author | González, MDLO | - |
dc.contributor.author | Jareño, F | - |
dc.contributor.author | Skinner, F | - |
dc.date.accessioned | 2020-05-19T15:13:34Z | - |
dc.date.available | 2020-05-19T15:13:34Z | - |
dc.date.issued | 2020-05-17 | - |
dc.identifier | ORCiD: María de la O González https://orcid.org/0000-0003-0740-7965 | - |
dc.identifier | ORCiD: Francisco Jareño https://orcid.org/0000-0001-9778-7345 | - |
dc.identifier | ORCiD: Frank S. Skinner https://orcid.org/0000-0002-1442-9479 | - |
dc.identifier | 810 | - |
dc.identifier.citation | González, M.D.L.O., Jareño, F. and Skinner, F. (2020) 'Nonlinear Autoregressive Distributed Lag Approach: An Application on the Connectedness between Bitcoin Returns and the Other Ten Most Relevant Cryptocurrency Returns', Mathematics, 8 (5), 810; pp. 1 - 22. doi: 10.3390/math8050810. | en_US |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/20856 | - |
dc.description.abstract | This article examines the connectedness between Bitcoin returns and returns of ten additional cryptocurrencies for several frequencies—daily, weekly, and monthly—over the period January 2015–March 2020 using a nonlinear autoregressive distributed lag (NARDL) approach. We find important and positive interdependencies among cryptocurrencies and significant long-run relationships among most of them. In addition, non-Bitcoin cryptocurrency returns seem to react in the same way to positive and negative changes in Bitcoin returns, obtaining strong evidence of asymmetry in the short run. Finally, our results show high persistence in the impact of both positive and negative changes in Bitcoin returns on most of the other cryptocurrency returns. Thus, our model explains about 50% of the other cryptocurrency returns with changes in Bitcoin returns. | en_US |
dc.description.sponsorship | Spanish Ministerio de Economía, Industria y Competitividad, grant number ECO2017-89715-P. | en_US |
dc.format.extent | 1 - 22 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | bitcoin | en_US |
dc.subject | cryptocurrencies | en_US |
dc.subject | NARDL | en_US |
dc.subject | connectedness | en_US |
dc.title | Nonlinear Autoregressive Distributed Lag Approach: An Application on the Connectedness between Bitcoin Returns and the Other Ten Most Relevant Cryptocurrency Returns | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2020-05-14 | - |
dc.identifier.doi | https://doi.org/10.3390/app14062253 | - |
dc.relation.isPartOf | Mathematics | - |
pubs.issue | 5 | - |
pubs.publication-status | Published | - |
pubs.volume | 8 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Economics and Finance Research Papers |
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FullText.pdf | Copyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 2.34 MB | Adobe PDF | View/Open |
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