European Banking Union structures and dynamics

This article begins with an analysis of banking flows in the euro zone, through a complex network, from 2006 to 2020. This analysis allows us to observe the topology of the network through different phases of the business cycle. It is obtained that there is greater fragmentation in the network that...

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Detalles Bibliográficos
Autor: Fernández Fernández, José Alejandro
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/114597
Acceso en línea:https://hdl.handle.net/20.500.14352/114597
Access Level:acceso abierto
Palabra clave:G01
G15
G21
G28
C63
C6
complex network
systemic risk
bank integration
bank flows
Bancos y cajas
5302 Econometría
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spelling European Banking Union structures and dynamicsFernández Fernández, José AlejandroG01G15G21G28C63C6complex networksystemic riskbank integrationbank flowsBancos y cajas5302 EconometríaThis article begins with an analysis of banking flows in the euro zone, through a complex network, from 2006 to 2020. This analysis allows us to observe the topology of the network through different phases of the business cycle. It is obtained that there is greater fragmentation in the network that increases in three stages, with turning points in crises. In turn, the topological structure is less random and presents more capitalized subnetworks with less risk. As for the nodes of the network, Germany gives up the position of centrality in favor of France. The determinants of the links in the network are analyzed with Machine Learning, obtaining as push and pull bank variables solvency and bank income structure, respectively, and productivity as economic variable.Taylor FrancisUniversidad Complutense de Madrid20232023-09-1220232023-09-12journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14352/114597reponame:Docta Complutenseinstname:Universidad Complutense de Madrid (UCM)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:docta.ucm.es:20.500.14352/1145972026-06-02T12:44:21Z
dc.title.none.fl_str_mv European Banking Union structures and dynamics
title European Banking Union structures and dynamics
spellingShingle European Banking Union structures and dynamics
Fernández Fernández, José Alejandro
G01
G15
G21
G28
C63
C6
complex network
systemic risk
bank integration
bank flows
Bancos y cajas
5302 Econometría
title_short European Banking Union structures and dynamics
title_full European Banking Union structures and dynamics
title_fullStr European Banking Union structures and dynamics
title_full_unstemmed European Banking Union structures and dynamics
title_sort European Banking Union structures and dynamics
dc.creator.none.fl_str_mv Fernández Fernández, José Alejandro
author Fernández Fernández, José Alejandro
author_facet Fernández Fernández, José Alejandro
author_role author
dc.contributor.none.fl_str_mv Universidad Complutense de Madrid
dc.subject.none.fl_str_mv G01
G15
G21
G28
C63
C6
complex network
systemic risk
bank integration
bank flows
Bancos y cajas
5302 Econometría
topic G01
G15
G21
G28
C63
C6
complex network
systemic risk
bank integration
bank flows
Bancos y cajas
5302 Econometría
description This article begins with an analysis of banking flows in the euro zone, through a complex network, from 2006 to 2020. This analysis allows us to observe the topology of the network through different phases of the business cycle. It is obtained that there is greater fragmentation in the network that increases in three stages, with turning points in crises. In turn, the topological structure is less random and presents more capitalized subnetworks with less risk. As for the nodes of the network, Germany gives up the position of centrality in favor of France. The determinants of the links in the network are analyzed with Machine Learning, obtaining as push and pull bank variables solvency and bank income structure, respectively, and productivity as economic variable.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-09-12
2023
2023-09-12
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14352/114597
url https://hdl.handle.net/20.500.14352/114597
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor Francis
publisher.none.fl_str_mv Taylor Francis
dc.source.none.fl_str_mv reponame:Docta Complutense
instname:Universidad Complutense de Madrid (UCM)
instname_str Universidad Complutense de Madrid (UCM)
reponame_str Docta Complutense
collection Docta Complutense
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,808905

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