TY - JOUR
T1 - A continuous updating weighted least squares estimator of tail dependence in high dimensions
AU - Einmahl, John H.J.
AU - Kiriliouk, Anna
AU - Segers, Johan
PY - 2018/6/1
Y1 - 2018/6/1
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 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.
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 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.
KW - Brown–Resnick process
KW - Extremal coefficient
KW - Max-linear model
KW - Multivariate extremes
KW - Stable tail dependence function
UR - http://www.scopus.com/inward/record.url?scp=85028769886&partnerID=8YFLogxK
U2 - 10.1007/s10687-017-0303-7
DO - 10.1007/s10687-017-0303-7
M3 - Article
AN - SCOPUS:85028769886
SN - 1386-1999
VL - 21
SP - 205
EP - 233
JO - Extremes
JF - Extremes
IS - 2
ER -