  The problem of estimating the L\'evy density of a partially observed
multidimensional affine process from low-frequency and mixed-frequency data is
considered. The estimation methodology is based on the log-affine
representation of the conditional characteristic function of an affine process
and local linear smoothing in time. We derive almost sure uniform rates of
convergence for the estimated L\'evy density both in mixed-frequency and
low-frequency setups and prove that these rates are optimal in the minimax
sense. Finally, the performance of the estimation algorithms is illustrated in
the case of the Bates stochastic volatility model.
