We apply distortion functions to bivariate survival functions for nonnegative random variables. This leads to a natural extension of univariate distortion risk measures to the multivariate setting. For Gini’s principle, the proportional hazard transform distortion and the dual power transform distortion, certain families of multivariate distributions lead to a straightforward risk measure. We show that an exact analytical expression can be obtained in some cases. We consider the independence case, the bivariate  Pareto distribution and the bivariate exponential distribution. An illustration of the estimation procedure and the interpretation is also included. In the case study, we consider two loss events with a single  risk value and monitor  the two events together over four different periods. We conclude that the dual power transform gives more weight to the observations of extreme losses, but that the distortion  parameter can modulate this influence in all cases. In our example, multivariate risk clearly diminishes over time.