D. Berg, Copula goodness-of-fit testing: an overview and power comparison, The European Journal of Finance, vol.95, issue.7-8, pp.675-701, 2009.
DOI : 10.1080/13518470802697428

A. Bücher, H. Dette, and S. Volgushev, New estimators of the Pickands dependence function and a test for extreme-value dependence. The Annals of Statistics, pp.1963-2006, 2011.

A. Bücher, J. Segers, and S. Volgushev, When uniform weak convergence fails: Empirical processes for dependence functions and residuals via epi- and hypographs, The Annals of Statistics, vol.42, issue.4, pp.1598-1634, 2014.
DOI : 10.1214/14-AOS1237SUPP

P. Capérà-a, A. L. Fougères, and C. Genest, A nonparametric estimation procedure for bivariate extreme value copulas, Biometrika, vol.84, issue.3, pp.567-577, 1997.
DOI : 10.1093/biomet/84.3.567

S. Coles, An introduction to statistical modeling of extreme values, 2001.
DOI : 10.1007/978-1-4471-3675-0

C. M. Cuadras and J. Augé, A continuous general multivariate distribution and its properties, Communications in Statistics - Theory and Methods, vol.37, issue.6, pp.339-353, 1981.
DOI : 10.1080/03610928108828042

P. Deheuvels, On the limiting behavior of the Pickands estimator for bivariate extreme-value distributions, Statistics & Probability Letters, vol.12, issue.5, pp.429-439, 1991.
DOI : 10.1016/0167-7152(91)90032-M

F. Durante and G. Salvadori, On the construction of multivariate extreme value models via copulas, Environmetrics, vol.14, issue.3, pp.143-161, 2010.
DOI : 10.1002/env.988

F. Durante and C. Sempi, Copula Theory: An Introduction, Copula Theory and Its Applications, pp.3-31, 2010.
DOI : 10.1007/978-3-642-12465-5_1

J. Einmahl, A. Krajina, and J. Segers, An M-estimator for tail dependence in arbitrary dimensions. The Annals of Statistics, pp.1764-1793, 2012.

M. Ferreira, Nonparametric estimation of the tail-dependence coefficient, REVSTAT?Statistical Journal, vol.11, issue.1, pp.1-16, 2013.

M. Fréchet, Remarques au sujet de la note précédente, CR Acad. Sci. Paris Sér. I Math, vol.246, pp.2719-2720, 1958.

C. Genest and A. C. Favre, Everything You Always Wanted to Know about Copula Modeling but Were Afraid to Ask, Journal of Hydrologic Engineering, vol.12, issue.4, pp.347-368, 2007.
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(347)
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.8266

C. Genest, K. Ghoudi, and L. P. Rivest, A semiparametric estimation procedure of dependence parameters in multivariate families of distributions, Biometrika, vol.82, issue.3, pp.543-552, 1995.
DOI : 10.1093/biomet/82.3.543

C. Genest, J. Ne?lehová, and N. B. Ghorbal, ESTIMATORS BASED ON KENDALL'S TAU IN MULTIVARIATE COPULA MODELS, Australian & New Zealand Journal of Statistics, vol.40, issue.2, pp.157-177, 2011.
DOI : 10.1111/j.1467-842X.2011.00622.x

C. Genest and B. Rémillard, Test of independence and randomness based on the empirical copula process, Test, vol.28, issue.2, pp.335-369, 2004.
DOI : 10.1007/BF02595777

C. Genest, B. Rémillard, and D. Beaudoin, Goodness-of-fit tests for copulas: A review and a power study, Insurance: Mathematics and Economics, vol.44, issue.2, pp.199-213, 2009.
DOI : 10.1016/j.insmatheco.2007.10.005

C. Genest and L. P. Rivest, Statistical Inference Procedures for Bivariate Archimedean Copulas, Journal of the American Statistical Association, vol.58, issue.423, pp.1034-1043, 1993.
DOI : 10.1214/aos/1176344685

C. Genest and J. Segers, Rank-based inference for bivariate extreme-value copulas. The Annals of Statistics, pp.2990-3022, 2009.

G. Gudendorf and J. Segers, Extreme-Value Copulas, Copula Theory and Its Applications, pp.127-145, 2010.
DOI : 10.1007/978-3-642-12465-5_6

P. Hall and N. Tajvidi, Distribution and Dependence-Function Estimation for Bivariate Extreme-Value Distributions, Bernoulli, vol.6, issue.5, pp.835-844, 2000.
DOI : 10.2307/3318758

L. P. Hansen, Large Sample Properties of Generalized Method of Moments Estimators, Econometrica, vol.50, issue.4, pp.1029-1054, 1982.
DOI : 10.2307/1912775

W. Hoeffding, A Class of Statistics with Asymptotically Normal Distribution, The Annals of Mathematical Statistics, vol.19, issue.3, pp.293-325, 1948.
DOI : 10.1214/aoms/1177730196

H. Joe, Multivariate models and dependence concepts, 2001.

C. Klüppelberg and G. Kuhn, Copula structure analysis, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.50, issue.3, pp.737-753, 2009.
DOI : 10.1111/j.1467-9868.2009.00707.x

I. Kojadinovic and J. Yan, A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems, Statistics and Computing, vol.40, issue.4, pp.17-30, 2011.
DOI : 10.1007/s11222-009-9142-y
URL : https://hal.archives-ouvertes.fr/hal-00868087

P. Krupskii and H. Joe, Factor copula models for multivariate data, Journal of Multivariate Analysis, vol.120, pp.85-101, 2013.
DOI : 10.1016/j.jmva.2013.05.001

G. Mazo, S. Girard, and F. Forbes, A flexible and tractable class of onefactor copulas, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00979147

R. B. Nelsen, An introduction to copulas, 2006.
DOI : 10.1007/978-1-4757-3076-0

. Flores, Kendall distribution functions, Statistics & Probability Letters, vol.65, issue.3, pp.263-268, 2003.

D. H. Oh and A. J. Patton, Simulated Method of Moments Estimation for Copula-Based Multivariate Models, Journal of the American Statistical Association, vol.41, issue.502, pp.689-700, 2013.
DOI : 10.1080/01621459.2013.785952

J. Pickands, Multivariate extreme value distributions, Proceedings of the 43rd Session of the International Statistical Institute, pp.859-878, 1981.

J. R. Schott, Matrix analysis for statistics, 2005.

J. Segers, Asymptotics of empirical copula processes under non-restrictive smoothness assumptions, Bernoulli, vol.18, issue.3, pp.764-782, 2012.
DOI : 10.3150/11-BEJ387

H. Tsukahara, Semiparametric estimation in copula models, Canadian Journal of Statistics, vol.87, issue.3, pp.357-375, 2005.
DOI : 10.1002/cjs.5540330304