L. B. Andersen and V. V. Piterbarg, Interest Rate Modeling, 2010.

S. Demarta and A. J. Mcneil, The t Copula and Related Copulas, International Statistical Review, vol.1, issue.1, pp.111-129, 2005.
DOI : 10.1111/j.1751-5823.2005.tb00254.x

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

G. Frahm, M. Junker, and A. Szimayer, Elliptical copulas: applicability and limitations, Statistics & Probability Letters, vol.63, issue.3, pp.275-286, 2003.
DOI : 10.1016/S0167-7152(03)00092-0

C. Genest, K. Ghoudi, and L. Rivest, ???Understanding Relationships Using Copulas,??? by Edward Frees and Emiliano Valdez, January 1998, North American Actuarial Journal, vol.82, issue.3, pp.143-149, 1998.
DOI : 10.1080/10485259408832593

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

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

M. Hofert, M. Mächler, and A. J. Mcneil, Archimedean copulas in high dimensions: Estimators and numerical challenges motivated by financial applications, Journal de la Société Française de Statistique, vol.154, issue.1, pp.25-63, 2012.

J. C. Huang, Cumulative distribution networks: Inference, estimation and applications of graphical models for cumulative distribution functions, 2009.

J. C. Huang and N. Jojic, Maximum-likelihood learning of cumulative distribution functions on graphs, Journal of Machine Learning Research W&CP Series, vol.9, pp.342-349, 2010.

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

D. Kim and J. M. Kim, Analysis of directional dependence using asymmetric copula-based regression models, Journal of Statistical Computation and Simulation, vol.34, issue.9, 1990.
DOI : 10.1016/j.jmva.2004.01.004

E. Liebscher, Construction of asymmetric multivariate copulas, Journal of Multivariate Analysis, vol.99, issue.10, pp.2234-2250, 2008.
DOI : 10.1016/j.jmva.2008.02.025

B. G. Lindsay, Composite likelihood methods, Contemporary Mathematics, vol.80, issue.1, pp.221-260, 1988.
DOI : 10.1090/conm/080/999014

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

M. Sklar, Fonctions de répartitionrépartitionà n dimensions et leurs marges Publications de l'Institut de Statistique de l, pp.229-231, 1959.

T. Van-pham and G. Mazo, PBC: product of bivariate copulas

L. Zhang and V. P. Singh, Gumbel???Hougaard Copula for Trivariate Rainfall Frequency Analysis, Journal of Hydrologic Engineering, vol.12, issue.4, pp.409-419, 2007.
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(409)

]. 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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.474.3734

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)

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, Statistics and Computing, 2015.
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

]. K. Aas, C. Czado, A. Frigessi, and H. Bakken, Pair-copula constructions of multiple dependence, Insurance: Mathematics and Economics, vol.44, issue.2, pp.182-198, 2009.
DOI : 10.1016/j.insmatheco.2007.02.001

E. F. Acar, C. Genest, and J. Ne?lehová, Beyond simplified pair-copula constructions, Journal of Multivariate Analysis, vol.110, pp.74-90, 2012.
DOI : 10.1016/j.jmva.2012.02.001

C. Amblard and S. Girard, A new extension of bivariate FGM copulas, Metrika, vol.8, issue.5, pp.1-17, 2009.
DOI : 10.1007/s00184-008-0174-7
URL : https://hal.archives-ouvertes.fr/inria-00134433

T. Bedford and R. M. Cooke, Probability density decomposition for conditionally dependent random variables modeled by vines, Annals of Mathematics and Artificial Intelligence, vol.32, issue.1/4, pp.245-268, 2001.
DOI : 10.1023/A:1016725902970

T. Bedford and R. M. Cooke, Vines?a new graphical model for dependent random variables. The Annals of Statistics, pp.1031-1068, 2002.

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

S. Demarta and A. J. Mcneil, The t Copula and Related Copulas, International Statistical Review, vol.1, issue.1, pp.111-129, 2005.
DOI : 10.1111/j.1751-5823.2005.tb00254.x

F. Durante, A new class of symmetric bivariate copulas, Journal of Nonparametric Statistics, vol.139, issue.7-8, pp.499-510, 2006.
DOI : 10.1016/j.jmva.2003.09.002

F. Durante and O. Okhrin, Estimation procedures for exchangeable marshall copulas with hydrological application. Stochastic Environmental Research and Risk Assessment, 2014.

F. Durante, J. J. Quesada-molina, and M. Flores, On a family of multivariate copulas for aggregation processes, Information Sciences, vol.177, issue.24, pp.5715-5724, 2007.
DOI : 10.1016/j.ins.2007.07.019

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

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

G. Frahm, M. Junker, and A. Szimayer, Elliptical copulas: applicability and limitations, Statistics & Probability Letters, vol.63, issue.3, pp.275-286, 2003.
DOI : 10.1016/S0167-7152(03)00092-0

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

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

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.

I. Kojadinovic, J. Segers, and J. Yan, Large-sample tests of extreme-value dependence for multivariate copulas, Canadian Journal of Statistics, vol.33, issue.3, pp.703-720, 2011.
DOI : 10.1002/cjs.10110
URL : https://hal.archives-ouvertes.fr/hal-00865055

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

D. Kurowicka and R. M. Cooke, DISTRIBUTION-FREE CONTINUOUS BAYESIAN BELIEF NETS, Proceedings of Mathematical methods in Reliability Conference, 2004.
DOI : 10.1142/9789812703378_0022

J. F. Mai and M. Scherer, L??vy-frailty copulas, Journal of Multivariate Analysis, vol.100, issue.7, pp.1567-1585, 2009.
DOI : 10.1016/j.jmva.2009.01.010

G. Mazo, S. Girard, and F. Forbes, Weighted least-squares inference for multivariate copulas based on dependence coefficients, ESAIM: Probability and Statistics, vol.19, 2014.
DOI : 10.1051/ps/2015014
URL : https://hal.archives-ouvertes.fr/hal-00979151

A. J. Mcneil, R. Frey, and P. Embrechts, Quantitative risk management: concepts, techniques, and tools, 2010.

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

R. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2013.

B. Renard and M. Lang, Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology, Advances in Water Resources, vol.30, issue.4, pp.897-912, 2007.
DOI : 10.1016/j.advwatres.2006.08.001
URL : https://hal.archives-ouvertes.fr/hal-00453788

. Dans-ce-casrho-de-spearman and . .. Tau-de-kendall, Nos travaux s'inscrivent dans un contexte plus global, dont nous faisons une synthèse ci-dessous. La recherche de modèles multivariés avec autant de qualités que possible était un sujet important il y a une dizaine d'années. Nous avons le sentiment, que, de nos jours, il en existe une gamme assez diversifiée Ces toutes dernières années, en particulier, ont vu apparaitre une classe de modèles très flexibles, les Vines Bien sûr, il reste encore beaucoup de recherche à effectuer, notamment pour rendre ces modèles plus parcimonieux, et mieux comprendre leur sensibilité par rapport au choix de la décomposition de la densité, ou des familles paramétriques bivariées à incorporer. En outre, le risque est grand, avec ces modèles, de sur-ajuster (overfit en anglais) les données. En ce sens, les modèles à facteur, et en particulier à un facteur (comme celui que nous avons proposé), sont très intéressants. Ils peuvent d'ailleurs être vus comme des modèles Vines « tronqués ». Nous aimerions terminer cette thèse par le questionnement « méta-statistique » suivant : que voulons-nous faire avec nos modèles ? Le but est-il vraiment d'ajuster les données le mieux possible, comme il en ressort l'impression à la lecture de certaines publications ? A notre avis, en grande dimension, la distribution que nous tentons de modéliser nous importe moins qu'une caractéristique de cette dernière. Autrement dit, c'est moins la loi de (X 1 , . . . , X d ) que celle de ?(X 1, avec ? : R d ? R, qui nous intéresse. Par exemple, dans le cas des applications en hydrologie que nous avons traitées à plusieurs reprises dans cette thèse

E. F. Acar, C. Genest, and J. Ne?lehová, Beyond simplified pair-copula constructions, Journal of Multivariate Analysis, vol.110, pp.74-90, 2012.
DOI : 10.1016/j.jmva.2012.02.001

C. Amblard and S. Girard, A new extension of bivariate FGM copulas, Metrika, vol.8, issue.5, pp.1-17, 2009.
DOI : 10.1007/s00184-008-0174-7
URL : https://hal.archives-ouvertes.fr/inria-00134433

T. Bedford and R. M. Cooke, Vines--a new graphical model for dependent random variables, The Annals of Statistics, vol.30, issue.4, pp.1031-1068, 2002.
DOI : 10.1214/aos/1031689016

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

L. Breiman, Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author), Statistical Science, vol.16, issue.3, pp.199-231, 2001.
DOI : 10.1214/ss/1009213726

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.

P. Capéraà, 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

D. G. Clayton, A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence, Biometrika, vol.65, issue.1, pp.141-151, 1978.
DOI : 10.1093/biomet/65.1.141

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

L. De-haan and A. Ferreira, Extreme value theory : an introduction, 2007.
DOI : 10.1007/0-387-34471-3

P. Deheuvels, A nonparametric test for independence. Publications de l'Institut de Statistique de l, pp.29-50, 1981.

S. Demarta and A. J. Mcneil, The t Copula and Related Copulas, International Statistical Review, vol.1, issue.1, pp.111-129, 2005.
DOI : 10.1111/j.1751-5823.2005.tb00254.x

F. Durante, A new class of symmetric bivariate copulas, Journal of Nonparametric Statistics, vol.139, issue.7-8, pp.499-510, 2006.
DOI : 10.1016/j.jmva.2003.09.002

F. Durante and O. Okhrin, Estimation procedures for exchangeable Marshall copulas with hydrological application. Stochastic Environmental Research and Risk Assessment, 2014.

F. Durante, J. J. Quesada-molina, and M. Ú. Flores, On a family of multivariate copulas for aggregation processes, Information Sciences, vol.177, issue.24, pp.5715-5724, 2007.
DOI : 10.1016/j.ins.2007.07.019

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

D. Fantazzini, The effects of misspecified marginals and copulas on computing the value at risk: A Monte Carlo study, Computational Statistics & Data Analysis, vol.53, issue.6, pp.2168-2188, 2009.
DOI : 10.1016/j.csda.2008.02.002

J. D. Fermanian, D. Radulovic, and M. Wegkamp, Weak convergence of empirical copula processes, Bernoulli, vol.10, issue.5, pp.847-860, 2004.
DOI : 10.3150/bj/1099579158

G. Frahm, M. Junker, and A. Szimayer, Elliptical copulas: applicability and limitations, Statistics & Probability Letters, vol.63, issue.3, pp.275-286, 2003.
DOI : 10.1016/S0167-7152(03)00092-0

M. Fréchet, Remarques au sujet de la note précédente Compte Rendu de l'Académie des Sciences de Paris, pp.2719-2720, 1958.

C. Genest and A. 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)

C. Genest and B. Rémillard, Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models, Annales de l'Institut Henri Poincaré : Probabilités et Statistiques, pp.1096-1127, 2008.
DOI : 10.1214/07-AIHP148

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

C. Genest and B. J. Werker, Conditions for the Asymptotic Semiparametric Efficiency of an Omnibus Estimator of Dependence Parameters in Copula Models, Proceedings of the Conference on Distributions With Given Marginals and Statistical Modelling, pp.103-112, 2002.
DOI : 10.1007/978-94-017-0061-0_12

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

G. Gudendorf and J. Segers, Nonparametric estimation of an extreme-value copula in arbitrary dimensions, Journal of Multivariate Analysis, vol.102, issue.1, pp.37-47, 2011.
DOI : 10.1016/j.jmva.2010.07.011

G. Gudendorf and J. Segers, Nonparametric estimation of multivariate extreme-value copulas, Journal of Statistical Planning and Inference, vol.142, issue.12, pp.3073-3085, 2012.
DOI : 10.1016/j.jspi.2012.05.007

I. H. Haff and J. Segers, Nonparametric estimation of pair-copula constructions with the empirical pair-copula. arXiv preprint :1201, 2012.

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

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

M. Hofert, Construction and Sampling of Nested Archimedean Copulas, Copula Theory and Its Applications, pp.147-160, 2010.
DOI : 10.1007/978-3-642-12465-5_7

M. Hofert, I. Kojadinovic, M. Maechler, and J. Yan, copula : Multivariate Dependence with Copulas, 2014. R package version 0, pp.999-1007

M. Hofert, M. Mächler, and A. J. Mcneil, Archimedean copulas in high dimensions : Estimators and numerical challenges motivated by financial applications, Journal de la Société Française de Statistique, vol.154, issue.1, pp.25-63, 2012.

M. Hofert and M. Scherer, CDO pricing with nested Archimedean copulas, Quantitative Finance, vol.11, issue.5, pp.775-787, 2011.
DOI : 10.1088/1469-7688/4/3/009

P. D. Hoff, X. Niu, and J. A. Wellner, Information bounds for Gaussian copulas, Bernoulli, vol.20, issue.2, pp.604-622, 2014.
DOI : 10.3150/12-BEJ499

J. C. Huang, N. Jojic, and C. Meek, Exact inference and learning for cumulative distribution functions on loopy graphs, Neural Information Processing Systems, 2010.

J. Li, K. Das, G. Fu, R. Li, and R. Wu, The Bayesian lasso for genome-wide association studies, Bioinformatics, vol.27, issue.4, pp.516-523, 2010.
DOI : 10.1093/bioinformatics/btq688

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

H. Joe, Dependence Modeling with Copulas, 2014.

C. H. Kimberling, A probabilistic interpretation of complete monotonicity, Aequationes Mathematicae, vol.10, issue.2-3, pp.152-164, 1974.
DOI : 10.1007/BF01832852

C. A. Klaassen and J. A. Wellner, Efficient Estimation in the Bivariate Normal Copula Model: Normal Margins Are Least Favourable, Bernoulli, vol.3, issue.1, pp.55-77, 1997.
DOI : 10.2307/3318652

I. Kojadinovic and J. Yan, copula : Multivariate dependence with copulas, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00868573

I. Kojadinovic, J. Yan, and M. Holmes, Fast large-sample goodness-of-fit tests for copulas, Statistica Sinica, vol.21, issue.2, pp.841-871, 2011.
DOI : 10.5705/ss.2011.037a
URL : https://hal.archives-ouvertes.fr/hal-00868175

D. Kundu and A. K. Dey, Estimating the parameters of the Marshall???Olkin bivariate Weibull distribution by EM algorithm, Computational Statistics & Data Analysis, vol.53, issue.4, pp.956-965, 2009.
DOI : 10.1016/j.csda.2008.11.009

D. Kurowicka, Introduction: Dependence Modeling, Dependence Modeling, Vine Copula Handbook. World Scientific, 2011.
DOI : 10.1142/9789814299886_0001

D. Kurowicka and R. M. Cooke, DISTRIBUTION-FREE CONTINUOUS BAYESIAN BELIEF NETS, Proceedings of Mathematical methods in Reliability Conference, 2004.
DOI : 10.1142/9789812703378_0022

O. Ledoit and M. Wolf, Improved estimation of the covariance matrix of stock returns with an application to portfolio selection, Journal of Empirical Finance, vol.10, issue.5, pp.603-621, 2003.
DOI : 10.1016/S0927-5398(03)00007-0

D. X. Li, On Default Correlation, The Journal of Fixed Income, vol.9, issue.4, pp.43-54, 2000.
DOI : 10.3905/jfi.2000.319253

E. Liebscher, Construction of asymmetric multivariate copulas, Journal of Multivariate Analysis, vol.99, issue.10, pp.2234-2250, 2008.
DOI : 10.1016/j.jmva.2008.02.025

F. Lindskog, A. Mcneil, and U. Schmock, Kendall???s Tau for Elliptical Distributions, 2003.
DOI : 10.1007/978-3-642-59365-9_8

A. W. Marshall and I. Olkin, A Multivariate Exponential Distribution, Journal of the American Statistical Association, vol.16, issue.317, pp.30-44, 1967.
DOI : 10.1080/01621459.1961.10482138

A. J. Mc and . Neil, Quantitative risk management : concepts, techniques and tools. Princeton series in finance, 2005.

A. J. Mcneil, R. Frey, and P. Embrechts, Quantitative risk management : concepts, techniques, and tools, 2010.

A. J. Mcneil and J. Ne?lehová, Multivariate Archimedean copulas, dmonotone functions and l1-norm symmetric distributions. The Annals of Statistics, pp.3059-3097, 2009.

A. J. Mcneil, Sampling nested Archimedean copulas, Journal of Statistical Computation and Simulation, vol.12, issue.3, pp.567-581, 2008.
DOI : 10.1080/00949650701255834

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

D. H. Oh and A. J. Patton, Modelling dependence in high dimensions with factor copulas, 2012.

O. Okhrin, Y. Okhrin, and W. Schmid, On the structure and estimation of hierarchical Archimedean copulas, Journal of Econometrics, vol.173, issue.2, pp.189-204, 2013.
DOI : 10.1016/j.jeconom.2012.12.001

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

J. F. Quessy, M. Said, and A. C. Favre, Multivariate Kendall's tau for change-point detection in copulas, Canadian Journal of Statistics, vol.77, issue.4, pp.65-82, 2013.
DOI : 10.1002/cjs.11150

R. Team, R : A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2013.

S. I. Resnick, Extreme values, regular variation, and point processes, 2007.
DOI : 10.1007/978-0-387-75953-1

G. Salvadori and C. De-michele, Estimating strategies for multiparameter Multivariate Extreme Value copulas, Hydrology and Earth System Sciences, vol.15, issue.1, pp.141-150, 2011.
DOI : 10.5194/hess-15-141-2011

C. Savu and M. Trede, Hierarchies of Archimedean copulas, Quantitative Finance, vol.10, issue.3, pp.295-304, 2010.
DOI : 10.1080/14697680902821733

F. Schmid and R. Schmidt, Multivariate extensions of Spearman's rho and related statistics, Statistics & Probability Letters, vol.77, issue.4, pp.407-416, 2007.
DOI : 10.1016/j.spl.2006.08.007

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

J. Segers, R. V. Akker, and B. J. Werker, Semiparametric Gaussian copula models : Geometry and rank-based efficient estimation. arXiv preprint :1306, 2013.

R. J. Serfling, Approximation theorems of mathematical statistics, 1980.
DOI : 10.1002/9780470316481

F. Serinaldi and S. Grimaldi, Fully Nested 3-Copula: Procedure and Application on Hydrological Data, Journal of Hydrologic Engineering, vol.12, issue.4, pp.420-430, 2007.
DOI : 10.1061/(ASCE)1084-0699(2007)12:4(420)

M. Sibuya, Bivariate extreme statistics, I, Annals of the Institute of Statistical Mathematics, vol.21, issue.2, pp.195-210, 1960.
DOI : 10.1007/BF01682329

A. Sklar, Fonction de répartition dont les marges sont données. Publications de l'Institut de Statistique de l, pp.229-231, 1959.

T. Van-pham and G. Mazo, PBC : product of bivariate copulas