Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/4144
Type: Artigo Científico
Title: Time-varying joint distribution through copulas
Author: Ausin, M. Concepcion
Lopes, Hedibert Freitas
Publication Date: 2010
Abstract: The analysis of temporal dependence in multivariate time series is considered. The dependence structure between the marginal series is modelled through the use of copulas which, unlike the correlation matrix, give a complete description of the joint distribution. The parameters of the copula function vary through time, following certain evolution equations depending on their previous values and the historical data. The marginal time series follow standard univariate GARCH models. Full Bayesian inference is developed where the whole set of model parameters is estimated simultaneously. This represents an essential difference from previous approaches in the literature where the marginal and the copula parameters are estimated separately in two consecutive steps. Moreover, a Bayesian procedure is proposed for the estimation of several measures of risk, such as the variance, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) of a portfolio of assets, providing point estimates and predictive intervals. The proposed copula model enables to capture the dependence structure between the individual assets which strongly influences these risk measures. Finally, the problem of optimal portfolio selection based on the estimation of mean–variance, mean–VaR and mean–CVaR efficient frontiers is also addressed. The proposed approach is illustrated with simulated and real financial time series.
Keywords (english terms): Não informado
Language: Inglês
CNPq Area: Ciências Sociais Aplicadas
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.
Appears in Collections:Coleção de Artigos Científicos

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