Spectral bootstrap confidence bands for Lévy-driven moving average processes

In this paper we study the problem of constructing bootstrap confidence intervals for the L\évy density of the driving L\évy process based on high-frequency observations of a L\évy-driven moving average processes. Using a spectral estimator of the L\évy density, we propose a novel implementations of multiplier and empirical bootstraps to construct confidence bands on a compact set away from the origin. We also provide conditions under which the confidence bands are asymptotically valid.