Nonparametric density estimation for the small jumps of Lévy processes

We consider the problem of estimating the density of the process associated with the small jumps of a pure jump L\évy process, possibly of infinite variation, from discrete observations of one trajectory. The interest of such a question lies on the observation that even when the L\évy measure is known, the density of the increments of the small jumps of the process cannot be computed. We discuss results both from low and high frequency observations. In a low frequency setting, assuming the L\évy density associated with the jumps larger than $\varepsilon\in (0,1]$ in absolute value is known, a spectral estimator relying on the convolution structure of the problem achieves minimax parametric rates of convergence with respect to the integrated $L_2$ loss, up to a logarithmic factor. In a high frequency setting, we remove the assumption on the knowledge of the L\évy measure of the large jumps and show that the rate of convergence depends both on the sampling scheme and on the behaviour of the L\évy measure in a neighborhood of zero. We show that the rate we find is minimax up to a log-factor. An adaptive penalized procedure is studied to select the cutoff parameter. These results are extended to encompass the case where a Brownian component is present in the L\évy process. Furthermore, we illustrate the performances of our procedures through an extensive simulation study.