Copula-based semiparametric models for multivariate time series
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Publication:443770
DOI10.1016/j.jmva.2012.03.001zbMath1281.62136OpenAlexW3123139161MaRDI QIDQ443770
Frédéric Soustra, Nicolas Papageorgiou, Bruno Rémillard
Publication date: 13 August 2012
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmva.2012.03.001
Estimation in multivariate analysis (62H12) Hypothesis testing in multivariate analysis (62H15) Stationary stochastic processes (60G10) Discrete-time Markov processes on general state spaces (60J05)
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Uses Software
Cites Work
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- Pair-copula constructions of multiple dependence
- Goodness-of-fit tests for copulas: A review and a power study
- Estimation of copula-based semiparametric time series models
- Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification
- A review of copula models for economic time series
- Time-dependent copulas
- An empirical central limit theorem with applications to copulas under weak dependence
- Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models
- Multivariate Archimedean copulas, \(d\)-monotone functions and \(\ell _{1}\)-norm symmetric distributions
- Basic properties of strong mixing conditions. A survey and some open questions
- On Kendall's process
- Vines -- a new graphical model for dependent random variables.
- Asymptotic theory of weakly dependent stochastic processes
- Statistical properties of parametric estimators for Markov chain vectors based on copula models
- Tests of independence and randomness based on the empirical copula process
- Dependence structure of conditional Archimedean copulas
- Bivariate option pricing using dynamic copula models
- The Structure of Bivariate Distributions
- Sampling nested Archimedean copulas
- Change analysis of a dynamic copula for measuring dependence in multivariate financial data
- Rank-Based Extensions of the Brock, Dechert, and Scheinkman Test
- Copula–Based Models for Financial Time Series
- Copules archimédiennes et families de lois bidimensionnelles dont les marges sont données
- Families of Multivariate Distributions
- A semiparametric estimation procedure of dependence parameters in multivariate families of distributions
- Copulas and Temporal Dependence
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