Determinantal point processes (DPPs) have recently proved to be a useful class of models in several areas of statistics, including spatial statistics, statistical learning or telecommunications networks. They are models for repulsive (or regular, or inhibitive) point processes, in the sense that nearby points of the process tend to repel each other. We consider two ways to quantify the repulsiveness of a point process, both based on its second order properties, and we address the question of how repulsive a stationary DPP can be. We exhibit the most repulsive stationary DPP, when the intensity is fixed. We investigate similarly the possible repulsiveness in the subclass of R-dependent stationary DPPs (for some fixed positive R), or equivalently DPPs with R-compactly supported kernels. Finally, in both the general case and the R-dependent case, we present some new parametric families of stationary DPPs that can cover all possible repulsiveness, from the homogeneous Poisson process (which induces no interaction) to the most repulsive DPP.