### Abstract

a goodness-of-t statistic whose asymptotic distribution is chi-square. Extensive Monte Carlo simulations confirm the excellent finite-sample performance of the estimator and demonstrate that it is a strong competitor to currently available methods. The estimator is then applied to disentangle sources of tail dependence in European stock markets.

Language | English |
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Place of Publication | TIlburg |

Publisher | CentER, Center for Economic Research |

Number of pages | 24 |

Volume | 2016-002 |

Publication status | Published - 18 Jan 2016 |

### Publication series

Name | CentER Discussion Paper |
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Volume | 2016-002 |

### Fingerprint

### Keywords

- Brown-resnick process
- extremal coefficient
- max-linear model
- multivariate extremes
- stable tail dependence function

### Cite this

*A Continuous Updating Weighted Least Squares Estimator of Tail Dependence in High Dimensions*. (CentER Discussion Paper; Vol. 2016-002). TIlburg: CentER, Center for Economic Research.

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**A Continuous Updating Weighted Least Squares Estimator of Tail Dependence in High Dimensions.** / Einmahl, John; Kiriliouk, A.; Segers, J.J.J.

Research output: Working paper › Discussion paper › Other research output

TY - UNPB

T1 - A Continuous Updating Weighted Least Squares Estimator of Tail Dependence in High Dimensions

AU - Einmahl, John

AU - Kiriliouk, A.

AU - Segers, J.J.J.

PY - 2016/1/18

Y1 - 2016/1/18

N2 - Likelihood-based procedures are a common way to estimate tail dependence parameters.They are not applicable, however, in non-differentiable models such as those arising from recent max-linear structural equation models. Moreover, they can be hard to compute in higher dimensions. An adaptive weighted least-squares procedure matching nonparametric estimates of the stable tail dependence function with the corresponding values of a parametrically specified proposal yields a novel minimum-distance estimator. The estimator is easy to calculate and applies to a wide range of sampling schemes and tail dependence models. In large samples, it is asymptotically normal with an explicit and estimable covariance matrix. The minimum distance obtained forms the basis ofa goodness-of-t statistic whose asymptotic distribution is chi-square. Extensive Monte Carlo simulations confirm the excellent finite-sample performance of the estimator and demonstrate that it is a strong competitor to currently available methods. The estimator is then applied to disentangle sources of tail dependence in European stock markets.

AB - Likelihood-based procedures are a common way to estimate tail dependence parameters.They are not applicable, however, in non-differentiable models such as those arising from recent max-linear structural equation models. Moreover, they can be hard to compute in higher dimensions. An adaptive weighted least-squares procedure matching nonparametric estimates of the stable tail dependence function with the corresponding values of a parametrically specified proposal yields a novel minimum-distance estimator. The estimator is easy to calculate and applies to a wide range of sampling schemes and tail dependence models. In large samples, it is asymptotically normal with an explicit and estimable covariance matrix. The minimum distance obtained forms the basis ofa goodness-of-t statistic whose asymptotic distribution is chi-square. Extensive Monte Carlo simulations confirm the excellent finite-sample performance of the estimator and demonstrate that it is a strong competitor to currently available methods. The estimator is then applied to disentangle sources of tail dependence in European stock markets.

KW - Brown-resnick process

KW - extremal coefficient

KW - max-linear model

KW - multivariate extremes

KW - stable tail dependence function

M3 - Discussion paper

VL - 2016-002

T3 - CentER Discussion Paper

BT - A Continuous Updating Weighted Least Squares Estimator of Tail Dependence in High Dimensions

PB - CentER, Center for Economic Research

CY - TIlburg

ER -