Bayesian Inference in Multivariate Stable Distributions Using Copulae
12 Pages Posted: 10 Feb 2013
Date Written: February 10, 2013
Abstract
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.
Keywords: Multivariate Stable Distributions, Bayesian inference, spectral measure, copula
JEL Classification: C11, C13
Suggested Citation: Suggested Citation
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