Operations Research Seminar - "High dimensional inference for extreme value indices" - Chen Zhou, Erasmus University Rotterdam

When applying multivariate extreme values statistics to analyze tail risk in compound events defined by a multivariate random vector, one often assumes that all dimensions share the same extreme value index. While such an assumption can be tested using a Wald-type test, the performance of such a test deteriorates as the dimensionality increases. This paper introduces a novel test for testing extreme value indices in a high dimensional setting. We show the asymptotic behavior of the test statistic and conduct simulation studies to evaluate its finite sample performance. The proposed test significantly outperforms existing methods in high dimensional settings. We apply this test to examine two datasets previously assumed to have identical extreme value indices across all dimensions.

Friday 22 November 2024 - 14h00 to 15h00

Anthropole 3088

Speaker(s): Chen Zhou Erasmus University Rotterdam

KEYWORDS
Published from 8 November 2024 to 22 November 2024
V. Chavez et F. Baeriswyl
Visibility:
published