A regularized goodness-of-fit test for copulas
[Un test d’adéquation de copule régularisé]
Journal de la société française de statistique, Numéro spécial sur les copules, Tome 154 (2013) no. 1, pp. 64-77.

Les auteurs proposent une statistique de type Anderson–Darling pour tester l’ajustement d’une copule. Ils déterminent la loi limite de la statistique sous l’hypothèse nulle. Puisque cette loi dépend de la valeur inconnue du paramètre de la copule, ils font appel à une approche par multiplicateurs pour le calcul du seuil du test. Ils évaluent la puissance du test par voie de simulation et trouvent qu’elle surpasse généralement celle du test de Cramér–von Mises fondé sur la distance entre la copule empirique et une estimation paramétrique de la copule convergente sous  0 .

The authors propose an Anderson–Darling-type statistic for copula goodness-of-fit testing. They determine the asymptotic distribution of the statistic under the null hypothesis. As this distribution depends on the unknown value of the copula parameter, they call on a multiplier method to compute the p -value of the test. They assess the power of the test through simulations and find that it is generally superior to that of the Cramér–von Mises statistic based on the distance between the empirical copula and a consistent parametric copula estimate under 0 .

Keywords: Anderson–Darling statistic, Cramér–von Mises statistic, empirical copula, Gaussian process, Monte Carlo study, Multiplier Central Limit Theorem, pseudo-observation, rank
Mot clés : statistique de Anderson–Darling, statistique de Cramér–von Mises, copule empirique, processus Gaussien, étude de Monte Carlo, théorème central limit à multiplicateurs, pseudo-observation, rang
@article{JSFS_2013__154_1_64_0,
     author = {Genest, Christian and Huang, Wanling and Dufour, Jean-Marie},
     title = {A regularized goodness-of-fit test for copulas},
     journal = {Journal de la soci\'et\'e fran\c{c}aise de statistique},
     pages = {64--77},
     publisher = {Soci\'et\'e fran\c{c}aise de statistique},
     volume = {154},
     number = {1},
     year = {2013},
     mrnumber = {3089616},
     zbl = {1316.62075},
     language = {en},
     url = {http://www.numdam.org/item/JSFS_2013__154_1_64_0/}
}
TY  - JOUR
AU  - Genest, Christian
AU  - Huang, Wanling
AU  - Dufour, Jean-Marie
TI  - A regularized goodness-of-fit test for copulas
JO  - Journal de la société française de statistique
PY  - 2013
SP  - 64
EP  - 77
VL  - 154
IS  - 1
PB  - Société française de statistique
UR  - http://www.numdam.org/item/JSFS_2013__154_1_64_0/
LA  - en
ID  - JSFS_2013__154_1_64_0
ER  - 
%0 Journal Article
%A Genest, Christian
%A Huang, Wanling
%A Dufour, Jean-Marie
%T A regularized goodness-of-fit test for copulas
%J Journal de la société française de statistique
%D 2013
%P 64-77
%V 154
%N 1
%I Société française de statistique
%U http://www.numdam.org/item/JSFS_2013__154_1_64_0/
%G en
%F JSFS_2013__154_1_64_0
Genest, Christian; Huang, Wanling; Dufour, Jean-Marie. A regularized goodness-of-fit test for copulas. Journal de la société française de statistique, Numéro spécial sur les copules, Tome 154 (2013) no. 1, pp. 64-77. http://www.numdam.org/item/JSFS_2013__154_1_64_0/

[1] D. Berg. Copula goodness-of-fit testing: An overview and power comparison. Europ. J. Finance, 5:675–701, 2009.

[2] D. Berg and J.-F. Quessy. Local power analyses of goodness-of-fit tests for copulas. Scand. J. Statist., 36:389–412, 2009. | MR | Zbl

[3] A. Bücher and H. Dette. A note on bootstrap approximations for the empirical copula process. Statist. Probab. Lett., 80:1925–1932, 2010. | MR | Zbl

[4] A. Bücher and S. Volgushev. Empirical and Sequential Empirical Copula Processes Under Serial Dependence. Unpublished manuscript, arXiv:1111.2778. | MR | Zbl

[5] M.A. Diouf, Statistical Analysis of Poverty and Inequalities. Doctoral dissertation, Université de Montréal, Canada, 2008.

[6] J.-D. Fermanian, D. Radulović, and M.H. Wegkamp. Weak convergence of empirical copula processes. Bernoulli, 10:847–860, 2004. | MR | Zbl

[7] P. Gänßler and W. Stute. Seminar on Empirical Processes. Birkhäuser, Basel, 1987. | Zbl

[8] C. Genest, A. Carabarín-Aguirre, and F. Harvey. Copula parameter estimation using Blomqvist’s beta. J. SFdS, 154:in press, 2013. | Numdam | MR

[9] C. Genest and A.-C. Favre. Everything you always wanted to know about copula modeling but were afraid to ask. J. Hydrol. Eng., 12:347–368, 2007.

[10] C. Genest, K. Ghoudi, and L.-P. Rivest. A semiparametric estimation procedure of dependence parameters in multivariate families of distributions. Biometrika, 82:543–552, 1995. | MR | Zbl

[11] C. Genest, J. Nešlehová, and J.-F. Quessy. Tests of symmetry for bivariate copulas. Ann. Inst. Statist. Math., 64:811–834, 2012. | MR

[12] C. Genest and B. Rémillard. Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models. Ann. Inst. H. Poincaré Probab. Statist., 44:1096–1127, 2008. | Numdam | MR | Zbl

[13] C. Genest, B. Rémillard, and D. Beaudoin. Omnibus goodness-of-fit tests for copulas: A review and a power study. Insurance Math. Econom., 44:199–213, 2009. | MR | Zbl

[14] K. Ghoudi and B. Rémillard. Empirical processes based on pseudo-observations. II. The multivariate case. In Asymptotic Methods in Stochastics, Fields Inst. Commun. 44:381–406, 2004. | MR | Zbl

[15] G. Kim, M.J. Silvapulle, and P. Silvapulle. Comparison of semiparametric and parametric methods for estimating copulas. Comput. Statist. Data Anal., 51:2836–2850, 2007. | MR | Zbl

[16] I. Kojadinovic and J. Yan. Comparison of three semiparametric methods for estimating dependence parameters in copula models. Insurance Math. Econom., 47:52–63, 2010. | MR | Zbl

[17] I. Kojadinovic and J. Yan. Modeling multivariate distributions with continuous margins using the copula R package. J. Statist. Software, 34:1–20, 2010.

[18] I. Kojadinovic and J. Yan. A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems. Stat. Comput., 21:17–30, 2011. | MR | Zbl

[19] I. Kojadinovic, J. Yan, and M. Holmes. Fast large-sample goodness-of-fit tests for copulas. Statist. Sinica, 21:841–871, 2011. | MR | Zbl

[20] M.R. Kosorok. Introduction to Empirical Processes and Semiparametric Inference. Springer, New York, 2008. | MR | Zbl

[21] A.J. McNeil, R. Frey, and P. Embrechts. Quantitative Risk Management: Concepts, Techniques and Tools. Princeton University Press, Princeton, NJ, 2005. | MR | Zbl

[22] J.R. Munkres. Topology: A First Course. Prentice-Hall, Englewood Cliffs, NJ, 1975. | MR

[23] R.B. Nelsen. An Introduction to Copulas, Second Edition. Springer, Berlin, 2006. | MR

[24] A.J. Patton. A review of copula models for economic time series. J. Multivariate Anal., 110:4–18, 2012. | MR | Zbl

[25] B. Rémillard and O. Scaillet. Testing for equality between two copulas. J. Multivariate Anal., 100:377–386, 2009. | Zbl

[26] L. Rüschendorf. Asymptotic distributions of multivariate rank order statistics. Ann. Statist., 4:912–923, 1976. | Zbl

[27] J. Segers. Asymptotics of empirical copula processes under nonrestrictive smoothness assumptions. Bernoulli, 18:764–782, 2012. | Zbl

[28] H. Tsukahara. Semiparametric estimation in copula models. Canad. J. Statist., 33:357–375, 2005. | Zbl