The aim of this volume is to provide an extensive account of the most
recent advances in statistics for discretely observed Lévy processes.
These days, statistics for stochastic processes is a lively topic,
driven by the needs of various fields of application, such as finance,
the biosciences, and telecommunication.
The three chapters of this volume are completely dedicated to the
estimation of Lévy processes, and are written by experts in the field.
The first chapter by Denis Belomestny and Markus Reiß treats the low
frequency situation, and estimation methods are based on the empirical
characteristic function. The second chapter by Fabienne Comte and Valery
Genon-Catalon is dedicated to non-parametric estimation mainly covering
the high-frequency data case. A distinctive feature of this part is the
construction of adaptive estimators, based on deconvolution or
projection or kernel methods. The last chapter by Hiroki Masuda
considers the parametric situation. The chapters cover the main aspects
of the estimation of discretely observed Lévy processes, when the
observation scheme is regular, from an up-to-date viewpoint.