Article ID Journal Published Year Pages File Type
1146752 Journal of Multivariate Analysis 2011 13 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Mathematics Numerical Analysis
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