P. Barbe, C. Genest, K. Ghoudi, R. , and B. , On Kendall's Process, Journal of Multivariate Analysis, vol.58, issue.2, pp.197-229, 1996.
DOI : 10.1006/jmva.1996.0048

M. Bargès, H. Cossette, and ´. E. Marceau, TVaR-based capital allocation with copulas, Insurance: Mathematics and Economics, vol.45, issue.3, 2009.
DOI : 10.1016/j.insmatheco.2009.08.002

J. Beirlant, Y. Goegebeur, J. Teugels, and J. Segers, Statistics of extremes, Wiley Series in Probability and Statistics Chichester. Theory and applications, With contributions from Daniel De Waal and Chris Ferro, 2004.
DOI : 10.1002/0470012382

F. Belzunce, A. Castaño, A. Olvera-cervantes, and A. Suárez-llorens, Quantile curves and dependence structure for bivariate distributions, Computational Statistics & Data Analysis, vol.51, issue.10, pp.515112-5129, 2007.
DOI : 10.1016/j.csda.2006.08.017

J. Cai and H. Li, Conditional tail expectations for multivariate phase-type distributions, Journal of Applied Probability, vol.2, issue.03, pp.810-825, 2005.
DOI : 10.1111/1467-9965.00068
URL : http://www.watrisq.uwaterloo.ca/IIPR/2005Reports/05.02.pdf

J. J. Cai, J. H. Einmahl, L. De-haan, and C. Zhou, Estimation of the marginal expected shortfall: the mean when a related variable is extreme, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.73, issue.2, pp.417-442, 2015.
DOI : 10.1016/j.insmatheco.2005.11.001

P. Capérà-a, A. Fougères, and C. Genest, A stochastic ordering based on a decomposition of Kendall's tau, Distributions with given marginals and moment problems, pp.81-86, 1996.

F. Chebana and T. Ouarda, Multivariate quantiles in hydrological frequency analysis, Environmetrics, vol.28, issue.3, pp.63-78, 2011.
DOI : 10.1214/aos/1016218226

A. Cousin, D. Bernardino, and E. , On multivariate extensions of Conditional-Tail-Expectation, Insurance: Mathematics and Economics, vol.55, issue.0, pp.55272-282, 2014.
DOI : 10.1016/j.insmatheco.2014.01.013
URL : https://hal.archives-ouvertes.fr/hal-00877386

L. De-haan and A. Ferreira, Extreme value theory. Springer Series in Operations Research and Financial Engineering, 2006.

L. De-haan and X. Huang, Large Quantile Estimation in a Multivariate Setting, Journal of Multivariate Analysis, vol.53, issue.2, pp.247-263, 1995.
DOI : 10.1006/jmva.1995.1035

D. Bernardino, E. Laloë, T. Maume-deschamps, V. Prieur, and C. , Plug-in estimation of level sets in a non-compact setting with applications in multivariable risk theory, ESAIM: Probability and Statistics, pp.236-256, 2013.

D. Bernardino, E. Palacios-rodríguez, and F. , Estimation of extreme quantiles conditioning on multivariate critical layers, Environmetrics, vol.9, issue.2, pp.158-168, 2016.
DOI : 10.5194/hessd-9-6781-2012
URL : https://hal.archives-ouvertes.fr/hal-01201727

D. Bernardino, E. Palacios-rodríguez, and F. , Estimation of extreme Component-wise Excess design realization: a hydrological application, Stochastic Environmental Research and Risk Assessment, 2017.
DOI : 10.1007/s00477-016-1249-z

D. Bernardino, E. Prieur, and C. , Estimation of multivariate conditional-tail-expectation using Kendall's process, Journal of Nonparametric Statistics, vol.8, issue.3, pp.241-267, 2014.
DOI : 10.1007/978-1-4757-2545-2

G. Draisma, H. Drees, A. Ferreira, D. Haan, and L. , Bivariate tail estimation: dependence in asymptotic independence, Bernoulli, vol.10, issue.2, pp.251-280, 2004.
DOI : 10.3150/bj/1082380219
URL : http://doi.org/10.3150/bj/1082380219

H. Drees and X. Huang, Best Attainable Rates of Convergence for Estimators of the Stable Tail Dependence Function, Journal of Multivariate Analysis, vol.64, issue.1, pp.25-47, 1998.
DOI : 10.1006/jmva.1997.1708

J. H. Einmahl, L. De-haan, L. , and D. , Weighted approximations of tail copula processes with application to testing the bivariate extreme value condition, The Annals of Statistics, vol.34, issue.4, pp.1987-2014, 2006.
DOI : 10.1214/009053606000000434

C. Genest, J. Quessy, R. , and B. , Goodness-of-fit Procedures for Copula Models Based on the Probability Integral Transformation, Scandinavian Journal of Statistics, vol.95, issue.2, pp.337-366, 2006.
DOI : 10.2307/2336591

C. Genest and L. Rivest, On the multivariate probability integral transformation, Statistics & Probability Letters, vol.53, issue.4, pp.391-399, 2001.
DOI : 10.1016/S0167-7152(01)00047-5

G. Gorge, Insurance Risk Management and Reinsurance. Gorge, 2013.

B. M. Hill, A Simple General Approach to Inference About the Tail of a Distribution, The Annals of Statistics, vol.3, issue.5, pp.1163-1174, 1975.
DOI : 10.1214/aos/1176343247

L. Hua and H. Joe, Second order regular variation and conditional tail expectation of multiple risks, Insurance: Mathematics and Economics, vol.49, issue.3, pp.537-546, 2011.
DOI : 10.1016/j.insmatheco.2011.08.013

Z. M. Landsman and E. A. Valdez, Tail Conditional Expectations for Elliptical Distributions, North American Actuarial Journal, vol.2, issue.2, pp.55-71, 2003.
DOI : 10.1080/10920277.1998.10595708
URL : http://wwwdocs.fce.unsw.edu.au/actuarial/research/papers/2002/ZinoviyValdez10.pdf

T. Mao and T. Hu, Second-order properties of the Haezendonck???Goovaerts risk measure for extreme risks, Insurance: Mathematics and Economics, vol.51, issue.2, 2012.
DOI : 10.1016/j.insmatheco.2012.06.003

G. Marcon, P. Naveau, and S. Padoan, A semi-parametric stochastic generator for bivariate extreme events, Stat, vol.25, issue.7, 2017.
DOI : 10.1002/joc.1175

R. B. Nelsen, An introduction to copulas, Lecture Notes in Statistics, vol.139, 1999.
DOI : 10.1007/978-1-4757-3076-0

R. B. Nelsen, J. J. Quesada-molinab, J. A. Rodríguez-lallenac, and M. Floresc, Kendall distribution functions, Statistics & Probability Letters, vol.65, issue.3, pp.263-268, 2003.
DOI : 10.1016/j.spl.2003.08.002
URL : http://www.ime.unicamp.br/~veronica/ME601701/paper2.pdf

G. Salvadori, C. De-michele, D. , and F. , On the return period and design in a multivariate framework, Hydrology and Earth System Sciences, vol.15, issue.11, pp.3293-3305, 2011.
DOI : 10.5194/hess-15-3293-2011

G. Salvadori, F. Durante, and E. Perrone, Semi-parametric approximation of Kendall's distribution function and multivariate Return Periods, Journal de la Société Française de Statistique, pp.151-173, 2013.

G. Salvadori, G. R. Tomasicchio, D. 'alessandro, and F. , Practical guidelines for multivariate analysis and design in coastal and off-shore engineering, Coastal Engineering, vol.88, pp.1-14, 2014.
DOI : 10.1016/j.coastaleng.2014.01.011

V. Singh, S. Jain, and A. Tyagi, Risk and Reliability Analysis, 2007.
DOI : 10.1061/9780784408919

M. Sklar, Fonctions de répartitionrépartition`répartitionà n dimensions et leurs marges, Publ. Inst. Statist. Univ. Paris, vol.8, pp.229-231, 1959.

R. Torres, C. De-michele, H. Laniado, and R. E. Lillo, Directional multivariate extremes in environmental phenomena, Environmetrics, vol.11, issue.5, p.2428, 2017.
DOI : 10.1061/(ASCE)1084-0699(2006)11:2(150)
URL : https://e-archivo.uc3m.es/bitstream/10016/23419/1/ws201610.pdf

A. W. Van-der-vaart and J. A. Wellner, Empirical processes indexed by estimated functions, Lecture Notes- Monograph Series, pp.234-252, 2007.
DOI : 10.1214/074921707000000382

S. Vandenberghe, M. J. Van-den-berg, B. Gräler, A. Petroselli, S. Grimaldi et al., Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation, Hydrology and Earth System Sciences, pp.6781-6828, 2012.
DOI : 10.5194/hessd-9-6781-2012

I. Weissman, Estimation of Parameters and Larger Quantiles Based on the k Largest Observations, Journal of the American Statistical Association, vol.73, issue.364, pp.73812-815, 1978.
DOI : 10.2307/2286285