Classification of Extremal Dependence in Financial Markets via Bootstrap Inference
Abstract
Accurately identifying the extremal dependence structure in multivariate heavy-tailed data is a fundamental yet challenging task, particularly in financial applications. Following a recently proposed bootstrap-based testing procedure, we apply the methodology to absolute log returns of U.S. S&P 500 and Chinese A-share stocks over a time period well before the U.S. election in 2024. The procedure reveals more isolated clustering of dependent assets in the U.S. economy compared with China which exhibits different characteristics and a more interconnected pattern of extremal dependence. Cross-market analysis identifies strong extremal linkages in sectors such as materials, consumer staples and consumer discretionary, highlighting the effectiveness of the testing procedure for large-scale empirical applications.
Cite
@article{arxiv.2506.04656,
title = {Classification of Extremal Dependence in Financial Markets via Bootstrap Inference},
author = {Qian Hui and Sidney I. Resnick and Tiandong Wang},
journal= {arXiv preprint arXiv:2506.04656},
year = {2025}
}