About small jumps of Lévy processes : approximations and estimation
In this talk we consider the problem of estimating the density of the process associated with the small jumps of a pure jump Lévy 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 Lévy 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 Lévy 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 Lévy measure of the large jumps and show that the rate of convergence depends both on the sampling scheme and on the behaviour of the Lévy 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 Lévy process. Furthermore, we illustrate the performances of our procedures through an extensive simulation study. This is a joint work with Taher Jalal and Céline Duval. This is a joint work with Taher Jalal and Céline Duval.
Area: CS19 - Advances in statistics for stochastic processes (Sara Mazzonetto)
Keywords: Lévy processes, minimax theory, adaptive estimators, lower bounds
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