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Bayesian Inference in Multivariate Stable Distributions Using Copulae

Efthymios G. Tsionas

出版	SSRN, 2013
URL	http://books.google.com.hk/books?id=yUMmzwEACAAJ&hl=&source=gbs_api

註釋

In this paper we take up Bayesian inference in multivariate stable distributions through innovative multivariate stable copulae. The problem that the characteristic function is defined through a difficult object, the spectral measure is completely bypassed by our approach. The new methods are applied to major exchange rates with encouraging results. The copula-based technique is based on non-parametric margins (both data-estimated as well as Dirichlet process priors) and we compare with a multivariate stable copula whose margins can be normal, Student-t or univariate stable.