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

Spectral estimation of the L\'evy density in partially observed affine models