Nonparametric Tests Dependence For Positive Quadrant

Michel Denuit and Olivier Scaillet
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Michel Denuit: Université Catholique de Louvain

FAME Research Paper Series from International Center for Financial Asset Management and Engineering

Abstract: We consider distributional free inference to test for positive quadrant dependence, i.e.for the probability that two variables are simultaneously small (or large) being at least as great as it would be were they dependent. Tests for its generalisation in higher dimensions, namely positive orthant dependences, are also analysed. We propose two types of testing procedures. The first procedure is based on the spcification of the dependence concepts in terms of distribution functions, while the second procedure exploits the copula representation. For each specification a distance test and an intersection-union test for inequality constraints are developed depending on the definition of null and alternative hypotheses. An empirical illustration is given for US and Danish insurance claim data. Practical implications for the design of reinsurance treaties are also discussed.

Keywords: Nonparametric; Stochastic Ordering; Positive Quadrant Dependence; Positive Orthant Dependence; Copula; Inequality constraint; Inequality constraint test; Risk management; Loss severity distribution (search for similar items in EconPapers)
JEL-codes: C12 D81 G10 G21 G22 (search for similar items in EconPapers)
Date: 2002-03
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Citations: View citations in EconPapers (1)

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