Einmahl, John H. J.
Kiriliouk, Anna
[UCL]
Segers, Johan
[UCL]
Likelihood-based procedures are a common way to estimate tail depen- dence 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 sam- ples, it is asymptotically normal with an explicit and estimable covariance matrix. The minimum distance obtained forms the basis of a goodness-of-fit 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.
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| Bibliographic reference |
Einmahl, John H. J. ; Kiriliouk, Anna ; Segers, Johan. A continuous updating weighted least squares estimator of tail dependence in high dimensions. In: Extremes : statistical theory and applications in science, engineering and economics, Vol. 21, no. 2, p. 205-233 (2018) |
| Permanent URL |
http://hdl.handle.net/2078.1/189240 |
