  The covariance matrix is well-known for its following properties: affine
equivariance, additivity, independence property and full affine equivariance.
Generalizing the first one leads into the study of scatter functionals,
commonly used as plug-in estimators to replace the covariance matrix in robust
statistics. However, if the application requires also some of the other
properties of the covariance matrix listed earlier, the success of the plug-in
depends on whether the candidate scatter functional possesses these. In this
short note we show that under natural regularity conditions the covariance
matrix is the only scatter functional that is additive and the only scatter
functional that is full affine equivariant.
