 A New Measure of Vector Dependence, with an Application to Financial C ontagionIvan Medovikov and Artem Prokhorov No 2016-01, Working Papers from University of Sydney Business School, Discipline of Business Analytics Abstract: We propose a new nonparametric measure of association between an arbitrary number of random vectors. The measure is based on the empirical copula process for the multivariate marginals, corresponding to the vectors, and is insensitive to the within-vector dependence. It is bounded by the [0, 1] interval, covering the entire range of dependence from vector independence to a vector version of a monotone relationship. We study the properties of the new measure under several well-known copulas and provide a nonparametric estimator of the measure, along with its asymptotic theory, under fairly general assumptions. To illustrate the applicability of the new measure, we use it to assess the degree of interdependence between equity markets in North and South America, Europe and Asia, surrounding the financial crisis of 2008. We find strong evidence of previously unknown contagion patterns, with selected regions exhibiting little dependence before and after the crisis and a lot of dependence during the crisis period. Keywords: Hoeffding's Phi-square; nonparametric statistics; measures of vector dependence; copula (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:syb:wpbsba:2123/14490 Access Statistics for this paper More papers in Working Papers from University of Sydney Business School, Discipline of Business Analytics Contact information at EDIRC. |
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