Classification of Extremal Dependence in Financial Markets via Bootstrap Inference

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 de‐GARCHed 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.