  This article studies nonparametric methods to estimate the co-integrated
volatility for multi-dimensional L\'evy processes with high frequency data. We
construct a spectral estimator for the co-integrated volatility and prove
minimax rates for an appropriate bounded nonparametric class of
semimartingales. Given $ n $ observations of increments over intervals of
length $1/n$, the rates of convergence are $1 / \sqrt{n} $ if $ r \leq 1 $ and
$ (n\log n)^{(r-2)/2} $ if $ r>1 $, which are optimal in a minimax sense. We
bound the co-jump index activity from below with the harmonic mean. Finally, we
assess the efficiency of our estimator by comparing it with estimators in the
existing literature.
