Journal of Multivariate Analysis
Comparison of semiparametric and parametric methods for estimating copulas
Computational Statistics & Data Analysis
A goodness of fit test for copulas based on Rosenblatt's transformation
Computational Statistics & Data Analysis
Copula model evaluation based on parametric bootstrap
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
An algorithm for maximizing Kendall's τ
Computational Statistics & Data Analysis
Vine copulas with asymmetric tail dependence and applications to financial return data
Computational Statistics & Data Analysis
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A two-stage semi-parametric estimation procedure for a broad class of copulas satisfying minimal regularity conditions has been recently proposed. In addition, a three-stage semi-parametric estimation method based on Kendall's tau in order to estimate the Student's t copula has also been designed. Its major advantage is to allow for greater computational tractability when dealing with high dimensional issues, where two-stage procedures are no more a viable choice. The asymptotic properties of this methodology are developed and its finite-sample behavior are examined via simulations. The advantages and disadvantages of this methodology are analyzed in terms of numerical convergence and positive definiteness of the estimated T-copula correlation matrix.