Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41455
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dc.contributor.authorAndrada-Félix, Juliánen_US
dc.contributor.authorFernandez-Perez, Adrianen_US
dc.contributor.authorSosvilla-Rivero, Simónen_US
dc.date.accessioned2018-07-03T15:54:53Z-
dc.date.available2018-07-03T15:54:53Z-
dc.date.issued2018en_US
dc.identifier.issn0003-6846en_US
dc.identifier.urihttp://hdl.handle.net/10553/41455-
dc.description.abstractThis study investigates the interconnection between five implied volatility indices representative of different financial markets during the period 1 August 2008–29 December 2017. To this end, we first perform a static and dynamic analysis to measure the total volatility connectedness in the entire period (the system-wide approach) using a framework recently proposed by Diebold and Yilmaz. Second, we make use of a dynamic analysis to evaluate both the net directional connectedness for each market and all net pairwise directional connectedness. Our results suggest that a 38.99%, of the total variance of the forecast errors is explained by shocks across markets, indicating that the remainder 61.01% of the variation is due to idiosyncratic shocks. Furthermore, we find that volatility connectedness varies over time, with a surge during periods of increasing economic and financial instability. Finally, we also document frequently switch between a net volatility transmitter and a net volatility receiver role in the five markets under study.en_US
dc.languageengen_US
dc.relation.ispartofApplied economics (Print)en_US
dc.sourceApplied Economics[ISSN 0003-6846],v. 50, p. 4234-4249en_US
dc.subject5302 Econometríaen_US
dc.subject.otherImplied Volatility
dc.subject.otherInformation-Content
dc.subject.otherFinancial Stress
dc.subject.otherEquity Markets
dc.subject.otherDebt Crisis
dc.subject.otherSpillovers
dc.subject.otherIndexes
dc.subject.otherReturns
dc.subject.otherRisk
dc.subject.otherGold
dc.titleFear connectedness among asset classesen_US
dc.typeinfo:eu-repo/semantics/Articlees
dc.typeArticlees
dc.identifier.doi10.1080/00036846.2018.1441521
dc.identifier.scopus85042378101
dc.identifier.isi000436000200003-
dc.contributor.authorscopusid6505916889
dc.contributor.authorscopusid50161225400
dc.contributor.authorscopusid6701863324
dc.description.lastpage4249-
dc.description.firstpage4234-
dc.relation.volume50
dc.investigacionCiencias Sociales y Jurídicasen_US
dc.type2Artículoen_US
dc.contributor.daisngid3014920
dc.contributor.daisngid2763673
dc.contributor.daisngid514725
dc.contributor.wosstandardWOS:Andrada-Felix, J
dc.contributor.wosstandardWOS:Fernandez-Perez, A
dc.contributor.wosstandardWOS:Sosvilla-Rivero, S
dc.date.coverdateAgosto 2018
dc.identifier.ulpgces
dc.description.sjr0,499
dc.description.jcr0,968
dc.description.sjrqQ2
dc.description.jcrqQ3
dc.description.ssciSSCI
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.author.deptGIR Finanzas Cuantitativas y Computacionales-
crisitem.author.deptDepartamento de Métodos Cuantitativos en Economía y Gestión-
crisitem.author.orcid0000-0001-8598-3234-
crisitem.author.parentorgDepartamento de Métodos Cuantitativos en Economía y Gestión-
crisitem.author.fullNameAndrada Félix, Julián-
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