Large‐sample tests of extreme‐value dependence for multivariate copulas

By Ivan Kojadinovic, Johan Segers, Jun Yan. 2011

60 citations recorded by the source corpus.

Starting from the characterization of extreme‐value copulas based on max‐stability, large‐sample tests of extreme‐value dependence for multivariate copulas are studied. The two key ingredients of the proposed tests are the empirical copula of the data and a multiplier technique for obtaining approximate p‐values for the derived statistics. The asymptotic validity of the multiplier approach is established, and the finite‐sample performance of a large number of candidate test statistics is studied through extensive Monte Carlo experiments for data sets of dimension two to five. In the bivariate case, the rejection rates of the best versions of the tests are compared with those of the test of Ghoudi et al. (1998) recently revisited by Ben Ghorbal et al. (2009). The proposed procedures are illustrated on bivariate financial data and trivariate geological data. The Canadian Journal of Statistics 39: 703–720; 2011. © 2011 Statistical Society of Canada

Subfields: Demography, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/972da335ea14246a1fa35bc38b849bf1610044d4

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Citations in the corpus

A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing (2013, unknown): “5 Estimation of the bandwidth parameter `n The bandwidth parameter `n defined in Assumption (A2) plays a role similar to that of the block length in the block bootstrap of Künsch (1989). Its value is therefore expected to have a crucial influence on the finite-sample performance of the dependent mul”
Multivariate Frequency-Severity Regression Models in Insurance (2016, background): “To determine whether a joint distribution corresponds to an Archimedean copula or a specific extreme-value copula, goodness-of-fit tests developed by [41–44] can be helpful.”
An Overview of the Goodness-of-Fit Test Problem for Copulas (2012, methodology): “By setting Dn(u) := √ n({Cn(u)}−Cn(u)), for all u ∈ [0,1]d and every r > 0, [61] have built some tests based on the limiting law of the joint process (Dn,r1 , .”