2025/06/05 by Hui Qian, Hui, Qian, Sidney I. Resnick +3
Decision Sciences · Economics, Econometrics and Finance · #China #Cluster analysis #Complex Systems and Time Series Analysis #Empirical research #FOS: Economics and business #FOS: Mathematics #Financial Risk and Volatility Modeling #Financial market #Inference #Multivariate statistics #Statistical Finance (q-fin.ST) #Statistics Theory (math.ST) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2506.04656
openalex publication_date 2025/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.