Extreme and Inference for Tail Gini Functionals With Applications in Tail Risk Measurement

• 成果类型: Article
• 署名作者: Hou, Yanxi; Wang, Xing
• 署名单位: Fudan University; Illinois Institute of Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1730855
• 发表日期: 2021
• 页码: 1428-1443
• 关键词: marginal expected shortfall; Systemic risk; portfolio

摘要

Tail risk analysis focuses on the problem of risk measurement on the tail regions of financial variables. As one crucial task in tail risk analysis for risk management, the measurement of tail risk variability is less addressed in the literature. Neither the theoretical results nor inference methods are fully developed, which results in the difficulty of modeling implementation. Practitioners are then short of measurement methods to understand and evaluate tail risks, even when they have large amounts of valuable data in hand. In this article, we consider the measurement of tail variability under the tail scenarios of a systemic variable by extending the Gini's methodology. As we are very interested in the limit of the proposed measures as the risk level approaches to the extreme status, we showed, by using extreme value techniques, how the tail dependence structure and marginal risk severity have influences on the limit of the proposed tail variability measures. We construct a nonparametric estimator, and its asymptotic behavior is explored. Furthermore, to provide practitioners with more measures for tail risk, we construct three coefficients/measures for tail risks from different views toward tail risks and illustrate them in a real data analysis. for this article are available online.