Stationary vine copula models for multivariate time series

Copula-based semiparametric models for multivariate time series

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) ...

Copula Methods for Forecasting Multivariate Time Series

Copula-based models provide a great deal of ‡exibility in modelling multivariate distributions, allowing the researcher to specify the models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. In addition to ‡exibility, this often also facilitates estimation of the model in stages, reducing the computational burden. Thi...

Copula - Based Models for Financial Time Series

This paper presents an overview of the literature on applications of copulas in the modelling of …nancial time series. Copulas have been used both in multivariate time series analysis, where they are used to charaterise the (conditional) cross-sectional dependence between individual time series, and in univariate time series analysis, where they are used to characterise the dependence between a...

Vine Copula Models with GLM and Sparsity

Vine copula provides a flexible tool to capture asymmetry in modelling multivariate distributions. Nevertheless, its flexibility is achieved at the expense of exponentially increasing complexity of the model. To alleviate this issue, the simplifying assumption (SA) is commonly adapted in specific applications of vine copula models. In this paper, generalized linear models (GLMs) are proposed fo...

Vine-copula Based Models for Farmland Portfolio Management