  We leverage the duality between risk-averse and distributionally robust
optimization (DRO) to devise a distributionally robust estimator that strictly
outperforms the empirical average for all probability distributions with
negative excess kurtosis. The aforesaid estimator solves the $\chi^{2}-$robust
mean squared error problem in closed form.
