We consider a consistent test, that is similar to a Kolmogorov-Smirnov test, of the complete set of restrictions that relate to the copula representation of positive quadrant dependence. For such a test we propose and justify inference relying on a simulation based multiplier method and a bootstrap method. We also explore the finite sample behavior of both methods with Monte Carlo experiments. A first empirical illustration is given for US insurance claim data. A second one examines the presence of positive quadrant dependence in life expectancies at birth of males and females among countries.

Keywords : Nonparametric, Positive Quadrant Dependence, Copula, Risk Management, Loss Severity Distribution, Bootstrap, Multiplier Method, Empirical Process.

JEL : C12, D81, G10, G21, G22.

AMS 2000 Subject Classification: 60E15, 62G10, 62G30, 62P05, 91B28, 91B30.