A structural break test for extremal dependence in β-mixing random vectors

• 成果类型: Article
• 署名作者: Hoga, Y.
• 署名单位: University of Duisburg Essen
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asy030
• 发表日期: 2018
• 页码: 627643
• 关键词: tail-dependence; time; diagnostics; coefficient; models; point

摘要

We derive a structural break test for extremal dependence in beta-mixing, possibly high-dimensional random vectors with either asymptotically dependent or asymptotically independent components. Existing tests require serially independent observations with asymptotically dependent components. To avoid estimating a long-run variance, we use self-normalization, which obviates the need to estimate the coefficient of tail dependence when components are asymptotically independent. Simulations show favourable empirical size and power of the test, which we apply to S&P 500 and DAX log-returns. We find evidence for one break in the coefficient of tail dependence for the upper and lower joint tail at the beginning of the 2007-08 financial crisis, leading to more extremal co-movement.