Estimating a multivariate Lévy density based on discrete observations

Existing results for the estimation of the Lévy measure are mostly limited to the onedimensional setting. We apply the spectral method to multidimensional Lévy processes in order to construct a nonparametric estimator for the multivariate jump distribution. We prove convergence rates for the uniform estimation error under both a low- and a high-frequency observation regime. The method is robust to various dependence structures. Along the way, we present a uniform risk bound for the multivariate empirical characteristic function and its partial derivatives. The method is illustrated with simulation examples.