发表机构
Rice University; Colorado State University（莱斯大学; 科罗拉多州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对股票相关矩阵估计的高斯Copula基准无法捕捉尾部依赖的缺陷，提出体制感知分解方法，在水、能源ETF成分上验证了其识别联合极端共同变动配对的有效性。
AI 中文摘要
股票相关矩阵是量化风险的基础，但对全样本拟合单一高斯Copula结构的常规默认做法存在结构性缺陷：它强制渐近尾部独立，因此无法代表驱动压力事件中风险的联合变动。本文指出，股票收益的依赖关系并非单一对象，而是在尾部、中心和上尾部三种不同条件下的三种不同形式，任何面向尾部敏感应用的估计器都应具备体制感知能力。我们提出一种分解方法，为每种体制拟合专用的依赖矩阵：在两个尾部采用基于多元正则变化的尾部依赖估计器，在中心体制采用时变条件相关模型，再通过无条件体制概率将三者重新组合形成稳态矩阵。该构造可识别高斯Copula基准在结构上抑制的尾部体制依赖。在水和能源部门的交易所交易基金（ETF）成分上进行验证，结果显示尾部体制重构了中心体制产生的商品划分，识别出高斯Copula默认做法系统性无法识别联合极端共同变动的配对。
英文摘要
Equity correlation matrices are foundational to quantitative risk, yet the operational default of a single Gaussian-copula structure fit to the full sample has a structural defect: it forces asymptotic tail-independence and so cannot represent the joint moves that drive risk in stress episodes. We argue that the dependence of equity returns is not one object but three distinct under lower-tail, central, and upper-tail conditions, and that any estimator aimed at tail-sensitive applications should be regime-aware. We develop a decomposition that fits a dedicated dependence matrix to each regime, using a tail-dependence estimator grounded in multivariate regular variation in the two tails and a time-varying conditional-correlation model in the central regime, and recombines the three at the unconditional regime probabilities to form a steady-state matrix. The construction identifies tail-regime dependence that the Gaussian-copula benchmark structurally suppresses. Demonstrated on water- and energy-sector Exchange Traded Funds (ETF) constituents, the tail regimes reorganize the commodity partition produced by the central regime, identifying the pairs where the Gaussian-copula default systematically fails to identify joint extreme co-movement.
