Parameter estimation for pair-copula constructions
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Publication:1952431
DOI10.3150/12-BEJ413zbMath1456.62033arXiv1303.4890OpenAlexW2030012614MaRDI QIDQ1952431
Publication date: 30 May 2013
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1303.4890
Asymptotic properties of parametric estimators (62F12) Nonparametric estimation (62G05) Point estimation (62F10) Characterization and structure theory for multivariate probability distributions; copulas (62H05)
Related Items (30)
Regime switches in the dependence structure of multidimensional financial data ⋮ Nonparametric estimation of pair-copula constructions with the empirical pair-copula ⋮ Structure learning in Bayesian networks using regular vines ⋮ Vine copula based likelihood estimation of dependence patterns in multivariate event time data ⋮ Simplified pair copula constructions -- limitations and extensions ⋮ Dynamic D-vine copula model with applications to Value-at-Risk (VaR) ⋮ Model robust inference with two-stage maximum likelihood estimation for copulas ⋮ A Time-Heterogeneous D-Vine Copula Model for Unbalanced and Unequally Spaced Longitudinal Data ⋮ Beyond Linear Dynamic Functional Connectivity: A Vine Copula Change Point Model ⋮ Penalized estimation of hierarchical Archimedean copula ⋮ Multivariate Markov families of copulas ⋮ Vine copula approximation: a generic method for coping with conditional dependence ⋮ Beyond simplified pair-copula constructions ⋮ Comparison of estimators for pair-copula constructions ⋮ On the weak convergence of the empirical conditional copula under a simplifying assumption ⋮ Copula directed acyclic graphs ⋮ Model selection in sparse high-dimensional vine copula models with an application to portfolio risk ⋮ Selecting and estimating regular vine copulae and application to financial returns ⋮ Estimating standard errors in regular vine copula models ⋮ Vine Copula Specifications for Stationary Multivariate Markov Chains ⋮ Testing the simplifying assumption in high-dimensional vine copulas ⋮ Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas ⋮ Stationary vine copula models for multivariate time series ⋮ On the quantification of aleatory and epistemic uncertainty using sliced-normal distributions ⋮ Copula approaches for modeling cross-sectional dependence of data breach losses ⋮ How simplifying and flexible is the simplifying assumption in pair-copula constructions -- analytic answers in dimension three and a glimpse beyond ⋮ Maximum likelihood estimation of mixed C-vines with application to exchange rates ⋮ Copula-based Black-Litterman portfolio optimization ⋮ Selection of Vine Copulas ⋮ A goodness-of-fit test for regular vine copula models
Uses Software
Cites Work
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