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dependence sign in to follow this
elliptical copula sign in to follow this
regular variation sign in to follow this
semi-parametric estimation sign in to follow this
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Semi-Parametric Models for the Multivariate Tail Dependence Function - The Asymptotically Dependent Case
Year Of Publication: 2007
Month Of Publication: May
Pages: 23
Download Count: 4
View Count: 1961
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 11-27-2010
Publisher: Administrator
Summary
In general, the risk of joint extreme outcomes in financial markets can be expressed as a function of the tail dependence function of a high-dimensional vector after standardizing marginals. Hence it is of importance to model and estimate tail dependence functions. Even for moderate dimension, nonparametrically estimating a tail dependence function is very inefficient and fitting a parametric model to tail dependence functions is not robust. In this paper we propose a semi-parametric model for (asymptotically dependent) tail dependence functions via an elliptical copula. Based on this model assumption, we propose a novel estimator for the tail dependence function, which proves favorable compared to the empirical tail dependence function estimator, both theoretically and empirically.
This document is published in Scandinavian Journal of Statistics,
(volume 35, number 4) December 2008, pp. 701-718.
Author(s)
Kluppelberg, Claudia Sign in to follow this author
Peng, Liang Sign in to follow this author
Kuhn, Gabriel Sign in to follow this author
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