Abstract:
Non-parametric estimation of the jumps of recurrent Markov processes and time-changed Lévy process is studied in this thesis. The law of their jumps is described by the Lévy kernel and Lévy measure, respectively. Based on discrete observations we construct an estimator for their density. We prove the consistency of our estimator and a central limit theorem. Practical aspects of our estimators are investigated in a simulation study.