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

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dc.contributor.authorNascimento, Diego Carvalho do
dc.contributor.authorXavier, Cleber
dc.contributor.authorFelipe, Israel José dos Santos
dc.contributor.authorLouzada Neto, Francisco
dc.date.accessioned2020-10-15T15:06:35Z
dc.date.available2020-10-15T15:06:35Z
dc.date.issued2019pt_BR
dc.description.abstractThe 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.pt_BR
dc.identifier.citationNASCIMENTO, D. C. et al. Dynamic conditional correlation GARCH: a multivariate time series novel using a bayesian approach. Journal of Modern Applied Statistical Methods, v. 18, n. 1, maio 2019. Disponível em: <https://digitalcommons.wayne.edu/jmasm/vol18/iss1/6/>. Acesso em: 27 set. 2020.pt_BR
dc.identifier.doihttp://dx.doi.org/10.22237/jmasm/1556669220pt_BR
dc.identifier.issn1538−9472
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/12846
dc.identifier.uri2https://digitalcommons.wayne.edu/jmasm/vol18/iss1/6/pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectVisual data miningpt_BR
dc.subjectFinancial contagionpt_BR
dc.titleDynamic conditional correlation GARCH : a multivariate time series novel using a bayesian approach.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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