A continuous updating weighted least squares estimator of tail dependence in high dimensions
(2018) Extremes : statistical theory and applications in science, engineering and economics — Vol. 21, n° 2, p. 205-233 (2018)
Files
document.pdf
Restricted Access - Adobe PDF
- 903.54 KB
Details
- Authors
- Abstract
- 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.
- Affiliations
Citations
Einmahl, J. H. J., Kiriliouk, A., & Segers, J. (2018). A continuous updating weighted least squares estimator of tail dependence in high dimensions. Extremes : statistical theory and applications in science, engineering and economics, 21(2), 205-233. https://doi.org/10.1007/s10687-017-0303-7 (Original work published 2018)