Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1146752 | Journal of Multivariate Analysis | 2011 | 13 Pages |
Abstract
We propose a parametric model for a bivariate stable Lévy process based on a Lévy copula as a dependence model. We estimate the parameters of the full bivariate model by maximum likelihood estimation. As an observation scheme we assume that we observe all jumps larger than some ε>0ε>0 and base our statistical analysis on the resulting compound Poisson process. We derive the Fisher information matrix and prove asymptotic normality of all estimates when the truncation point ε→0ε→0. A simulation study investigates the loss of efficiency because of the truncation.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Habib Esmaeili, Claudia Klüppelberg,