Large-sample tests of extreme-value dependence for multivariate copulas
(2011) Canadian Journal of Statistics — Vol. 39, n° 4, p. 703-720 (2011)
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- Authors
- Abstract
- 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.
- Affiliations
Université de Pau et des Pays de l’Adour, Pau, France Laboratoire de Mathématiques et Applications University of Connecticut, USA Department of Statistics
Citations
Kojadinovic, I., Segers, J., & Yan, J. (2011). Large-sample tests of extreme-value dependence for multivariate copulas. Canadian Journal of Statistics, 39(4), 703-720. https://doi.org/10.1002/cjs.10110 (Original work published 2011)