Symmetry and dependence properties within a semiparametric family of bivariate copulas

By Cécile Amblard, Stéphane Girard. 2002

106 citations recorded by the source corpus.

In this paper, we study a semiparametric family of bivariate copulas. The family is generated by a univariate function, determining the symmetry (radial symmetry, joint symmetry) and dependence property (quadrant dependence, total positivity, \ldots ) of the copulas. We provide bounds on different measures of association (such as Kendall's Tau, Spearman's Rho) for this family and several choices of generating functions for which these bounds can be reached.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/412e541661f4b8237ddcd3ccf9ca87ac3684f410

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fabrizio Durante
2. Manuel Úbeda-Flores
3. Stéphane Girard
4. Ali Dolati
5. Carlo Sempi

In Finance

1. Fabrizio Durante
2. Carlo Sempi
3. Manuel Úbeda-Flores
4. Stéphane Girard
5. Cécile Amblard
6. Ali Dolati
7. Laurent Gardes
8. Gilles Stupfler

In Statistics and Probability

1. Fabrizio Durante
2. Carlo Sempi
3. Ali Dolati
4. Manuel Úbeda-Flores
5. Stéphane Girard
6. Cécile Amblard
7. Laurent Gardes
8. G. Salvadori

Citations in the corpus

• A new extension of bivariate FGM copulas (2009, background): “It was first introduced in [24], and extensively studied in [1,2].”
• Copula Theory: An Introduction (2010, background): “More recent investigations are also provided in [4, 5, 6, 65, 169].”
• On Measures of Multivariate Concordance (2006, background): “u; v/ 2 II, studied by Amblard and Girard [1] (for a complete study of this generalization, see Dolati and Úbeda-Flores [4]).”