Copula-based semiparametric models for multivariate time series

Bruno Rémillard, Nicolas Papageorgiou and Frédéric Soustra

Journal of Multivariate Analysis, 2012, vol. 110, issue C, 30-42

Abstract: The authors extend to multivariate contexts the copula-based univariate time series modeling approach of Chen & Fan [X. Chen, Y. Fan, Estimation of copula-based semiparametric time series models, J. Econometrics 130 (2006) 307–335; X. Chen, Y. Fan, Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification, J. Econometrics 135 (2006) 125–154]. In so doing, they tackle simultaneously serial dependence and interdependence between time series. Their technique differs from the usual approach to time series copula modeling in which the series are first modeled individually and copulas are used to model the dependence between their innovations. The authors discuss parameter estimation and goodness-of-fit testing for their model, with emphasis on meta-elliptical and Archimedean copulas. The method is illustrated with data on the Canadian/US exchange rate and the value of oil futures over a ten-year period.

Keywords: Conditional copulas; Markov models; Pseudo likelihood; Ranks (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (28)

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DOI: 10.1016/j.jmva.2012.03.001

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