Inference for vast dimensional elliptical distributions
Dominicy, Yves Ogata, Hiroaki Veredas, David
Dominicy, Yves
Ogata, Hiroaki
Veredas, David
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Publication Type
Journal article
Editor
Supervisor
Publication Year
2013
Journal
Computational Statistics
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Publication Volume
28
Publication Issue
4
Publication Begin page
1853
Publication End page
1880
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Abstract
We propose a quantile–based method to estimate the parameters of an elliptical distribution, and a battery of tests for model adequacy. The method is suitable for vast dimensions as the estimators for location and dispersion have closed–form expressions, while estimation of the tail index boils down to univariate optimizations. The tests for model adequacy are for the null hypothesis of correct specification of one or several level contours. A Monte Carlo study to three distributions (Gaussian, Student–t and elliptical stable) for dimensions 20, 200 and 2000 reveals the goodness of the method, both in terms of computational time and finite samples. An empirical application to financial data illustrates the method.
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Accounting & Finance