| Abstract: | We consider distributional free inference to test for positivequadrant dependence, that is, for the probability that two variablesare simultaneously small (or large) being at least as greatas it would be were they dependent. Tests for its generalizationto higher dimensions, namely positive orthant dependence, arealso analyzed. We propose two types of testing procedures. Thefirst procedure is based on the specification of the dependenceconcepts in terms of distribution functions, while the secondprocedure exploits the copula representation. For each specification,a distance test and an intersection-union test for inequalityconstraints are developed for time-dependent data. An empiricalillustration is given for U.S. insurance claim data, where wediscuss practical implications for the design of reinsurancetreaties. Another application concerns detection of positivequadrant dependence between the HFR and CSFB/Tremont marketneutral hedge fund indices and the S&P 500 index. |
