标题： 金融市场的极值依赖性分类通过自助推断 显示英文标题

标题： Classification of Extremal Dependence in Financial Markets via Bootstrap Inference

摘要： 准确识别多元重尾数据中的极值相依结构是一项基础但具有挑战性的任务，尤其是在金融应用中。 根据最近提出的一种基于自助法的检验程序，我们将该方法应用于2024年美国大选前一段时间内的美国标普500指数和中国A股股票的绝对对数收益率。 结果显示，与表现出不同特征且具有更紧密的极值相依模式的中国经济相比，美国经济中存在更多孤立的相依资产聚类现象。 跨市场分析表明，在材料、日常消费品和可选消费品等行业中存在较强的极值关联性，这凸显了该检验程序在大规模经验研究中的有效性。 显示英文摘要

摘要： 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.