Time series copula models using d-vines and v-transforms

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...

Flexible pair-copula estimation in D-vines using bivariate penalized splines

Copula and semicopula transforms

The notion of copula was introduced by Sklar [24] who proved the theorem that now bears his name; it is commonly used in probability and statistics (see, for instance, [19, 22, 23]). Later, in order to characterize a class of operations on distribution functions that derive from operations on random variables defined on the same probability space, Alsina et al. [1] introduced the notion of quas...

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) ...

Estimation of copula-based semiparametric time series models

This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric marginal distributions and parametric copula functions, while the copulas capture all the scale-free temporal dependence of the processes. Simple estimators of the marginal distribution and the copula parameter are provided, and their asymptotic p...