Identifying Mixture Copula Components Using Outlier Detection Methods and Goodness-of-Fit Tests

Journal of Risk, Forthcoming

48 Pages Posted: 16 Sep 2011 Last revised: 24 Oct 2013

See all articles by Gregor N. F. Weiss

Gregor N. F. Weiss

University of Leipzig - Faculty of Economics and Management Science

Multiple version iconThere are 2 versions of this paper

Date Written: August 30, 2013

Abstract

This paper proposes the use of outlier detection methods from robust statistics and copula goodness-of-fit tests to identify components of mixture copulas. We first consider simulated data samples in which the true dependence structure is given by a mixture of two parametric copulas: one copula that is presumed to represent the true dependence structure and one disturbing copula. The Monte Carlo simulations show that the goodness-of-fit tests we consider lose significantly in power when applied to mixtures of copulas with different tail dependence. Several goodness-of-fit tests are shown to hold their nominal level when multivariate outliers are excluded, although this improvement comes at the price of a further loss in the tests' power. The usefulness of excluding outliers in copula goodness-of-fit testing is exemplified in an empirical risk management application.

Keywords: Dependence structures; copulas; goodness-of-fit-testing; robustness

JEL Classification: G11, C13, C15

Suggested Citation

Weiss, Gregor N. F., Identifying Mixture Copula Components Using Outlier Detection Methods and Goodness-of-Fit Tests (August 30, 2013). Journal of Risk, Forthcoming, Available at SSRN: https://ssrn.com/abstract=1927881 or http://dx.doi.org/10.2139/ssrn.1927881

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