Classification of Extremal Dependence in Financial Markets via Bootstrap Inference

ArXiv ID: 2506.04656 “View on arXiv”

Authors: Qian Hui, Sidney I. Resnick, Tiandong Wang

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.

Keywords: extremal dependence, bootstrap testing, heavy-tailed data, cross-market analysis, risk clustering, Equity (US & Chinese)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced extreme value theory and asymptotic statistics, evidenced by formulas for angular measures and bootstrap-based hypothesis tests, indicating high mathematical density. It also demonstrates strong empirical rigor by applying the methodology to real-world financial datasets from the U.S. and China, conducting cross-market sectoral analysis, and using a structured bootstrap procedure with tuning parameters for large-scale classification.

  flowchart TD
    A["Research Goal:<br>Identify Extremal Dependence<br>in Financial Markets"] --> B["Methodology: Bootstrap Testing<br>on Multivariate Heavy-Tailed Data"]
    B --> C["Data Inputs:<br>US S&P 500 & Chinese A-Share<br>Absolute Log Returns"]
    C --> D{"Computational Process:<br>Bootstrap Inference Testing"}
    D --> E["US Markets Outcome:<br>Isolated Clustering of Assets"]
    D --> F["China Markets Outcome:<br>Interconnected Pattern of Dependence"]
    E & F --> G["Cross-Market Analysis:<br>Strong Linkages in Materials,<br>Consumer Staples, & Discretionary"]
    G --> H["Final Finding:<br>Effective Procedure for<br>Large-Scale Risk Analysis"]