A simple condition for the multivariate CLT and the attraction to the Gaussian of Lévy processes at long and short times

We show that a necessary and sufficient condition for the sum of iid random vectors to converge (under appropriate centering and scaling) to a multivariate Gaussian distribution is that the truncated second moment matrix is slowly varying at infinity. This is more natural than the standard conditions, and allows for the possibility that the limiting Gaussian distribution is degenerate (so long as it is not concentrated at a point). We also give necessary and sufficient conditions for a d-dimensional Lévy process to converge (under appropriate centering and scaling) to a multivariate Gaussian distribution as time approaches zero or infinity.