Abstract
In this paper, we propose a nonparametric estimator of ruin probability in a Lévy risk model. The aggregate
claims process X = {Xt , ≥ 0} is modeled by a pure-jump Lévy process. Assume that high-frequency
observed data on X are available. The estimator is constructed based on the Pollaczek–Khinchin formula
and Fourier transform. Risk bounds as well as a data-driven cut-off selection methodology are presented.
Simulation studies are also given to show the finite sample performance of our estimator
claims process X = {Xt , ≥ 0} is modeled by a pure-jump Lévy process. Assume that high-frequency
observed data on X are available. The estimator is constructed based on the Pollaczek–Khinchin formula
and Fourier transform. Risk bounds as well as a data-driven cut-off selection methodology are presented.
Simulation studies are also given to show the finite sample performance of our estimator
| Original language | English |
|---|---|
| Pages (from-to) | 24-35 |
| Journal | Insurance: Mathematics and Economics |
| Volume | 53 |
| DOIs | |
| Publication status | Published - 2013 |
| Externally published | Yes |