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Classification of Extremal Dependence in Financial Markets via Bootstrap Inference

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  • 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.

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  • Qian Hui & Sidney I. Resnick & Tiandong Wang, 2025. "Classification of Extremal Dependence in Financial Markets via Bootstrap Inference," Papers 2506.04656, arXiv.org.
    • Handle: RePEc:arx:papers:2506.04656
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    3. Shuang Hu & Zuoxiang Peng & Johan Segers, 2024. "Modeling multivariate extreme value distributions via Markov trees," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 760-800, June.
    4. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    5. Lehtomaa, Jaakko & Resnick, Sidney I., 2020. "Asymptotic independence and support detection techniques for heavy-tailed multivariate data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 262-277.
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