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dc.contributor.authorGalán Camacho, Jorge E.
dc.contributor.authorRodríguez Moreno, María
dc.date.accessioned2020-11-27T07:10:45Z
dc.date.available2020-11-27T07:10:45Z
dc.date.issued2020-11
dc.identifier.issn1579-3621 (edición electrónica)
dc.identifier.urihttps://repositorio.bde.es/handle/123456789/14224
dc.descriptionArtículo de revista
dc.description.abstractFinancial stability is aimed at preventing and mitigating systemic risk, which is largely associated to the tail risk of macrofinancial variables. In this context, policy makers need to consider not only the most likely (central tendency) future path of macrofinancial variables, but also the distribution of all possible outcomes about that path, and focus on the downside risk. Against this background, the so-called at-risk methods provide a useful framework for the assessment of financial stability by the recognition of non-linear effects on the distribution of macrofinancial variables. We describe the use of quantile regressions for this purpose and illustrate two empirical applications related to the house prices and the GDP, from which useful insights for policymakers are derived.
dc.format.extent26 p.
dc.language.isoeng
dc.publisherBanco de España
dc.relation.ispartofRevista de estabilidad financiera. Nº 39 (otoño 2020), p. 71-96
dc.relation.hasversionOtra versión 123456789/14232
dc.rightsReconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
dc.rightsIn Copyright - Non Commercial Use Permitted
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/deed.es_ES
dc.rights.urihttp://rightsstatements.org/vocab/InC-NC/1.0/
dc.titleAt-risk measures and financial stability
dc.typeArtículo
dc.identifier.bdebib000470588
dc.identifier.bdepubREFI-2020-39-3
dc.subject.bdeFluctuaciones : previsiones y simulaciones
dc.subject.bdePredicción
dc.subject.bdeRegulación y supervisión de instituciones financieras
dc.publisher.bdeMadrid : Banco de España, 2020
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