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A note on nonparametric estimation of bivariate tail dependence
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Axel Bücher
Veröffentlicht/Copyright:
28. Mai 2014
Abstract
Nonparametric estimation of tail dependence can be based on a standardization of the marginals if their cumulative distribution functions are known. In this paper it is shown to be asymptotically more efficient if the additional knowledge of the marginals is ignored and estimators are based on ranks. The discrepancy between the two estimators is shown to be substantial for the popular Clayton and Gumbel–Hougaard models. A brief simulation study indicates that the asymptotic conclusions transfer to finite samples.
Keywords: Asymptotic variance; nonparametric estimation; rank-based inference; tail copula; tail dependence
AMS (2010): Primary 62G32; Secondary 62G05
Received: 2013-1-16
Revised: 2014-7-7
Accepted: 2013-8-4
Published Online: 2014-5-28
Published in Print: 2014-6-28
©2014 Walter de Gruyter Berlin/Boston
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Artikel in diesem Heft
- Frontmatter
- Asymptotic results for the regression function estimate on continuous time stationary and ergodic data
- A note on nonparametric estimation of bivariate tail dependence
- Prediction of regionalized car insurance risks based on control variates
- Stochastic orderings with respect to a capacity and an application to a financial optimization problem
Schlagwörter für diesen Artikel
Asymptotic variance;
nonparametric estimation;
rank-based inference;
tail copula;
tail dependence
Artikel in diesem Heft
- Frontmatter
- Asymptotic results for the regression function estimate on continuous time stationary and ergodic data
- A note on nonparametric estimation of bivariate tail dependence
- Prediction of regionalized car insurance risks based on control variates
- Stochastic orderings with respect to a capacity and an application to a financial optimization problem
