A continuous updating weighted least squares estimator of tail dependence in high dimensions

TL;DR

This paper introduces a new, easy-to-compute weighted least squares estimator for tail dependence in high-dimensional models, outperforming existing methods and applicable to various sampling schemes.

Contribution

It proposes a novel adaptive minimum-distance estimator for tail dependence that is computationally simple, broadly applicable, and asymptotically normal with an explicit covariance matrix.

Findings

Estimator shows excellent finite-sample performance in simulations.

It is a strong competitor to existing methods.

Applied successfully to analyze tail dependence in European stock markets.

Abstract

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 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.