Dynamic conditional correlation GARCH : a multivariate time series novel using a bayesian approach.

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorNascimento, Diego Carvalho do-
Autor(es): dc.creatorXavier, Cleber-
Autor(es): dc.creatorFelipe, Israel José dos Santos-
Autor(es): dc.creatorLouzada Neto, Francisco-
Data de aceite: dc.date.accessioned2022-02-21T19:59:28Z-
Data de disponibilização: dc.date.available2022-02-21T19:59:28Z-
Data de envio: dc.date.issued2020-10-15-
Data de envio: dc.date.issued2020-10-15-
Data de envio: dc.date.issued2019-
Fonte completa do material: dc.identifierhttp://www.repositorio.ufop.br/handle/123456789/12846-
Fonte completa do material: dc.identifierhttps://digitalcommons.wayne.edu/jmasm/vol18/iss1/6/-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.22237/jmasm/1556669220-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/650842-
Descrição: dc.descriptionThe Dynamic Conditional Correlation GARCH (DCC-GARCH) mutation model is considered using a Monte Carlo approach via Markov chains in the estimation of parameters, time-dependence variation is visually demonstrated. Fifteen indices were analyzed from the main financial markets of developed and developing countries from different continents. The performances of indices are similar, with a joint evolution. Most index returns, especially SPX and NDX, evolve over time with a higher positive correlation.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Direitos: dc.rightsrestrito-
Palavras-chave: dc.subjectVisual data mining-
Palavras-chave: dc.subjectFinancial contagion-
Título: dc.titleDynamic conditional correlation GARCH : a multivariate time series novel using a bayesian approach.-
Aparece nas coleções:Repositório Institucional - UFOP

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