It is known that a bivariate extreme value distribution (EVD) GG with reverse exponential margins can be represented as G(x,y)=exp(-||(x,y)||)G(x,y)=\exp(-||(x,y)||), x,y £ 0x,y\le 0, where ||||||\cdot|| is a suitable norm on
\mathbbR2\mathbb{R}^2. We prove in this paper the converse implication, i.e., given an arbitrary norm ||||||\cdot|| on
\mathbbR2\mathbb{R}^2,
... [Show full abstract] G(x,y):=exp(-||(x,y)||)G(x,y):=\exp(-||(x,y)||), x,y £ 0x,y\le 0, defines an EVD with reverse exponential margins, if and only if the norm satisfies for z Î [0,1]z\in[0,1] the condition max(z,1-z) £ ||(z,1-z)|| £ 1\max(z,1-z)\le ||(z,1-z)||\le 1. This result is extended to bivariate EVDs with arbitrary margins as well as to extreme value copulas. By identifying an EVD G(x,y)=exp(-||(x,y)||)G(x,y)=\exp(-||(x,y)||), x,y £ 0x,y\le 0, with the unit ball corresponding to the generating norm ||||||\cdot||, we obtain a characterization of the class of EVDs GG in terms of compact and convex subsets of
\mathbbR2\mathbb{R}^2.