Parametric estimation of a bivariate stable Lévy process

Habib Esmaeili and Claudia Klüppelberg

Journal of Multivariate Analysis, 2011, vol. 102, issue 5, 918-930

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 [epsilon]>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 [epsilon]-->0. A simulation study investigates the loss of efficiency because of the truncation.

Keywords: Levy; copula; Maximum; likelihood; estimation; Dependence; structure; Fisher; information; matrix; Multivariate; stable; process; Parameter; estimation (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (4)

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