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分类依据与纠错

AI 根据所提供的论文材料归类:摘要明确研究系统压力事件、尾部风险指标,并应用于道琼斯股票风险衡量。

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正在核对关联版本…

代码、数据与补充材料

正在核对对应版本的资源…

关注这篇论文的作者
Liujun Chen
Deyuan Li
Zhengjun Zhang

系统性与共同风险驱动的边际期望副作用

研究问题
当共同风险指标或最差成员指标触发全系统压力事件时,如何衡量各成员承受的边际期望副作用并进行统计估计?
方法与思路
作者扩展全系统压力事件的定义,构建 SYS2MES 指标,并在多元极值理论框架下处理尾部依赖与独立情形。
材料陈述的贡献
摘要称提出指标估计量及其渐近理论,以模拟评估有限样本表现,并通过道琼斯股票和美国疫情数据展示应用。
阅读关注点
应核对压力事件和阈值的选择、两个触发指标如何组合,以及尾部条件、估计不确定性和现有指标的比较标准。
阅读边界
仅据版本未注明的 OpenAlex 索引摘要;具体尾部假设和阈值敏感性未说明,摘要所称更有意义并非普遍优越性证明。

原文摘要

System-wide stress events (SWSE) can occur in social, economic, financial, environmental, medical, and many other systems. An SWSE can cause severe damage to some or all members in the system. Understanding and measuring the marginal expected side-effect (MES) for each member caused by an SWSE can lead to better risk management and decision-making. This paper defines an SWSE as a scenario in which either a representative systematic index or the index of the worst-performing member (systemic index) in a system exceeds its respective prescribed threshold. This broadened SWSE definition introduces an innovative systemic and systematic risks-driven marginal expected side-effect (SYS2MES) index. We construct estimators for SYS2MES and establish their asymptotic theories within the multivariate extreme value theory framework, including tail dependent and tail independent scenarios, thereby enhancing its applicability to a wide range of models. The finite sample performance of the estimators is investigated in a simulation study. The advantages of SYS2MES are demonstrated by studying its application to Dow Jones’ 30 stocks and COVID-19 data in the United States, yielding more meaningful results than existing approaches.

笔记与分组
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