B. Abdous and B. Remillard, Relating quantiles and expectiles under weighted-symmetry, Annals of the Institute of Statistical Mathematics, vol.69, issue.2, pp.371-384, 1995.
DOI : 10.1007/BF00773468

C. Acerbi, Spectral measures of risk: A coherent representation of subjective risk aversion, Journal of Banking & Finance, vol.26, issue.7, pp.1505-1518, 2002.
DOI : 10.1016/S0378-4266(02)00281-9

C. Acerbi and D. Tasche, On the coherence of expected shortfall, Journal of Banking & Finance, vol.26, issue.7, pp.1487-1503, 2002.
DOI : 10.1016/S0378-4266(02)00283-2

P. Artzner, F. Delbaen, J. Eber, and D. Heath, Coherent Measures of Risk, Mathematical Finance, vol.9, issue.3, pp.203-228, 1999.
DOI : 10.1111/1467-9965.00068

J. Beirlant, Y. Goegebeur, J. Segers, and J. Teugels, Statistics of Extremes: Theory and Applications, 2004.
DOI : 10.1002/0470012382

F. Bellini, B. Klar, A. Müller, and E. R. Gianina, Generalized quantiles as risk measures, Insurance: Mathematics and Economics, vol.54, pp.41-48, 2014.
DOI : 10.1016/j.insmatheco.2013.10.015

F. Bellini, D. Bernardino, and E. , Risk management with expectiles, The European Journal of Finance, vol.6, issue.3, pp.487-506, 2017.
DOI : 10.1016/j.jspi.2013.12.004

J. Breckling and R. Chambers, -quantiles, Biometrika, vol.75, issue.4, pp.761-772, 1988.
DOI : 10.1093/biomet/75.4.761

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

J. Daníelsson, P. Embrechts, C. Goodhart, C. Keating, F. Muennich et al., An Academic Response to Basel II. Special paper no. 130, Financial Markets Group, 2001.

A. Daouia, I. Gijbels, and G. Stupfler, Extremiles: A new perspective on asymmetric least squares Available at https, J. Amer. Statist. Assoc, 2017.

A. Daouia, S. Girard, and G. Stupfler, Estimation of tail risk based on extreme expectiles, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.26, issue.2, pp.263-292, 2018.
DOI : 10.1111/mafi.12080
URL : https://hal.archives-ouvertes.fr/hal-01311778

L. De-haan and A. Ferreira, Extreme Value Theory: An Introduction, 2006.
DOI : 10.1007/0-387-34471-3

L. De-haan, C. Mercadier, and C. Zhou, Adapting extreme value statistics to financial time series: dealing with bias and serial dependence, Finance and Stochastics, vol.73, issue.2, pp.321-354, 2016.
DOI : 10.1016/j.jempfin.2014.08.005
URL : https://hal.archives-ouvertes.fr/hal-01159376

D. Dietrich, L. De-haan, and J. Hüsler, Testing extreme value conditions, Extremes, pp.71-85, 2002.

H. Drees, On smooth statistical tail functionals, Scand, J. Stat, vol.25, pp.187-210, 1998.

H. Drees, L. De-haan, and D. Li, Approximations to the tail empirical distribution function with application to testing extreme value conditions, Journal of Statistical Planning and Inference, vol.136, issue.10, pp.3498-3538, 2006.
DOI : 10.1016/j.jspi.2005.02.017

H. Drees, L. De-haan, and S. Resnick, How to make a Hill plot, The Annals of Statistics, vol.28, issue.1, pp.254-274, 2000.
DOI : 10.1214/aos/1016120372

W. Ehm, T. Gneiting, A. Jordan, and F. Krüger, Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.5, issue.3, pp.505-562, 2016.
DOI : 10.1175/2010MWR3229.1

E. Methni, J. Gardes, L. Girard, and S. , Nonparametric estimation of extreme risks from conditional heavy-tailed distributions, Scand, J. Stat, vol.41, pp.988-1012, 2014.

E. Methni, J. Stupfler, and G. , Extreme versions of Wang risk measures and their estimation for heavy-tailed distributions, pp.907-930, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01313675

E. Methni, J. Stupfler, and G. , Improved estimators of extreme Wang distortion risk measures for very heavy-tailed distributions, Econometrics and Statistics, vol.6, pp.129-148, 2018.
DOI : 10.1016/j.ecosta.2017.03.002
URL : https://hal.archives-ouvertes.fr/hal-01406342

P. Embrechts, C. Klüppelberg, and T. Mikosch, Modelling of extremal events in insurance and finance, ZOR Zeitschrift f???r Operations Research Mathematical Methods of Operations Research, vol.73, issue.1, 1997.
DOI : 10.1007/978-3-662-02847-6

P. Hall and A. W. Welsh, Adaptive Estimates of Parameters of Regular Variation, The Annals of Statistics, vol.13, issue.1, pp.331-341, 1985.
DOI : 10.1214/aos/1176346596
URL : http://doi.org/10.1214/aos/1176346596

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

H. Holzmann and B. Klar, Expectile asymptotics, Electronic Journal of Statistics, vol.10, issue.2, pp.2355-2371, 2016.
DOI : 10.1214/16-EJS1173
URL : http://doi.org/10.1214/16-ejs1173

J. Hüsler and D. Li, On testing extreme value conditions, Extremes, pp.69-86, 2006.

R. Koenker and G. S. Bassett, Regression Quantiles, Econometrica, vol.46, issue.1, pp.33-50, 1978.
DOI : 10.2307/1913643

V. Krätschmer and H. Zähle, Statistical inference for expectile-based risk measures , Scand, J. Stat, vol.44, pp.425-454, 2017.

C. Kuan, J. Yeh, and Y. Hsu, Assessing value at risk with CARE, the Conditional Autoregressive Expectile models, Journal of Econometrics, vol.150, issue.2, pp.261-270, 2009.
DOI : 10.1016/j.jeconom.2008.12.002

T. Mao, K. Ng, and T. Hu, ASYMPTOTIC EXPANSIONS OF GENERALIZED QUANTILES AND EXPECTILES FOR EXTREME RISKS, Probability in the Engineering and Informational Sciences, vol.54, issue.03, pp.309-327, 2015.
DOI : 10.1016/j.jeconom.2008.12.002

T. Mao and F. Yang, Risk concentration based on Expectiles for extreme risks under FGM copula, Insurance: Mathematics and Economics, vol.64, pp.429-439, 2015.
DOI : 10.1016/j.insmatheco.2015.06.009

W. K. Newey and J. L. Powell, Asymmetric Least Squares Estimation and Testing, Econometrica, vol.55, issue.4, pp.819-847, 1987.
DOI : 10.2307/1911031

S. Resnick, Heavy-Tail Phenomena: Probabilistic and Statistical Modeling, 2007.

S. Resnick and C. St?-aric?-a, Smoothing the Hill Estimator, Advances in Applied Probability, vol.12, issue.01, pp.271-293, 1997.
DOI : 10.1214/aos/1176349551
URL : http://ecommons.cornell.edu/bitstream/1813/8996/1/TR001114.pdf

R. T. Rockafellar and S. Uryasev, Optimization of conditional value-at-risk, The Journal of Risk, vol.2, issue.3, pp.21-42, 2000.
DOI : 10.21314/JOR.2000.038
URL : http://www.smartquant.com/references/var/var20.pdf

R. T. Rockafellar and S. Uryasev, Conditional value-at-risk for general loss distributions, Journal of Banking & Finance, vol.26, issue.7, pp.1443-1471, 2002.
DOI : 10.1016/S0378-4266(02)00271-6
URL : http://www.math.washington.edu/~rtr/papers/rtr-CVaR2.pdf

F. Sobotka and T. Kneib, Geoadditive expectile regression, Computational Statistics & Data Analysis, vol.56, issue.4, pp.755-767, 2012.
DOI : 10.1016/j.csda.2010.11.015

J. Taylor, Estimating Value at Risk and Expected Shortfall Using Expectiles, Journal of Financial Econometrics, vol.6, issue.2, pp.231-252, 2008.
DOI : 10.1093/jjfinec/nbn001

B. Vandewalle and J. Beirlant, On univariate extreme value statistics and the estimation of reinsurance premiums, Insurance: Mathematics and Economics, vol.38, issue.3, pp.441-459, 2006.
DOI : 10.1016/j.insmatheco.2005.11.002

S. S. Wang, Abstract, ASTIN Bulletin, vol.50, issue.01, pp.71-92, 1996.
DOI : 10.2143/AST.21.2.2005365
URL : https://hal.archives-ouvertes.fr/hal-01351397

I. Weissman, Estimation of parameters and large quantiles based on the k largest observations, J. Amer. Statist. Assoc, vol.73, pp.812-815, 1978.
DOI : 10.1080/01621459.1978.10480104

J. L. Wirch and M. R. Hardy, A synthesis of risk measures for capital adequacy, Insurance: Mathematics and Economics, vol.25, issue.3, pp.337-347, 1999.
DOI : 10.1016/S0167-6687(99)00036-0

J. F. Ziegel, COHERENCE AND ELICITABILITY, Mathematical Finance, vol.16, issue.3, pp.901-918, 2016.
DOI : 10.1111/j.1467-9965.2006.00277.x

T. Zwingmann and H. Holzmann, Asymptotics for the expected shortfall, 2016.