Nonparametric density estimation for the small jumps of Lévy processes

Céline Duval (), Jalal Taher () and Ester Mariucci ()
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Céline Duval: Sorbonne Université
Jalal Taher: Université Paris-Saclay, UVSQ, CNRS, Laboratoire de mathématiques de Versailles
Ester Mariucci: Université Paris-Saclay, UVSQ, CNRS, Laboratoire de mathématiques de Versailles

Statistical Inference for Stochastic Processes, 2025, vol. 28, issue 3, No 2, 26 pages

Abstract: Abstract 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 in closed-form. 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 $$\varepsilon \in (0,1]$$ ε ∈ ( 0 , 1 ] in absolute value is known, a spectral estimator relying on the convolution structure of the problem achieves a parametric rate of convergence with respect to the integrated $$L_2$$ 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 logarithmic 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 numerically illustrate the performances of our procedures.

Keywords: Deconvolution; Lévy processes; Small jumps; Infinitely divisible distributions; Minimax rates of convergence; 62G07; 60G51; 60E07; 62G20; 62C20; 62M05 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11203-025-09331-y

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