scholarly journals Archimedean Copula Estimation Parameter with Kendall Distribution Function

2017 ◽  
Vol 38 (4) ◽  
pp. 619-625
Author(s):  
Ayşe METIN KARAKAS ◽  
Murat KARAKAS ◽  
Mine DOGAN
2021 ◽  
Vol 2 (3) ◽  
pp. 52-60
Author(s):  
N. Idiou ◽  
F. Benatia

In this paper, we look at two different approaches methodologies for copula estimation. The first is based on a parametric approach using MLE and IFM methods, while the second is entirely based on Kendall's tau and spearman's rho in a semi-parametric context, where the margins are estimated non-parametrically. Interestingly, based on R software simulation techniques, the contribution of their algorithms, approach, and illustration was our main focus for this paper. As an application, a class of Archimedean copulas was notably chosen. This particular class of copulas was also presented for censored data to show the estimator's performance even better.


2019 ◽  
Vol 56 (3) ◽  
pp. 858-869
Author(s):  
Michael Falk ◽  
Simone A. Padoan ◽  
Florian Wisheckel

AbstractConsider a random vector $\textbf{U}$ whose distribution function coincides in its upper tail with that of an Archimedean copula. We report the fact that the conditional distribution of $\textbf{U}$ , conditional on one of its components, has under a mild condition on the generator function independent upper tails, no matter what the unconditional tail behavior is. This finding is extended to Archimax copulas.


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