Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.04656 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915327597608960 |
|---|---|
| author | Hui, Qian Resnick, Sidney I. Wang, Tiandong |
| author_facet | Hui, Qian Resnick, Sidney I. Wang, Tiandong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04656 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classification of Extremal Dependence in Financial Markets via Bootstrap Inference Hui, Qian Resnick, Sidney I. Wang, Tiandong Statistics Theory Statistical Finance 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. |
| title | Classification of Extremal Dependence in Financial Markets via Bootstrap Inference |
| topic | Statistics Theory Statistical Finance |
| url | https://arxiv.org/abs/2506.04656 |