Journal
JOURNAL OF MULTIVARIATE ANALYSIS
Volume 101, Issue 10, Pages 2571-2586Publisher
ELSEVIER INC
DOI: 10.1016/j.jmva.2010.07.006
Keywords
Multivariate measure of association; Copula; Nonparametric estimation; Empirical copula process; Weak convergence; Nonparametric bootstrap; Strong mixing
Categories
Funding
- Deutsche Forschungsgemeinschaft (DFG)
Ask authors/readers for more resources
A multivariate measure of association is proposed, which extends the bivariate copula-based measure Phi-Square introduced by Hoeffding [22]. We discuss its analytical properties and calculate its explicit value for some copulas of simple form; a simulation procedure to approximate its value is provided otherwise. A nonparametric estimator for multivariate Phi-Square is derived and its asymptotic behavior is established based on the weak convergence of the empirical copula process both in the case of independent observations and dependent observations from strictly stationary strong mixing sequences. The asymptotic variance of the estimator can be estimated by means of nonparametric bootstrap methods. For illustration, the theoretical results are applied to financial asset return data. (c) 2010 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available