Classification of Extremal Dependence in Financial Markets via Bootstrap Inference

TL;DR

This paper introduces a bootstrap-based method to classify extremal dependence structures in financial markets, revealing differences in asset clustering and interdependence in U.S. and Chinese stock markets.

Contribution

The paper applies a novel bootstrap testing procedure to identify extremal dependence structures in financial data, demonstrating its effectiveness in large-scale empirical analysis.

Findings

U.S. stocks show more isolated extremal clusters.

Chinese stocks exhibit more interconnected extremal dependence.

Strong sector linkages identified in materials and consumer sectors.

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.