Measuring and testing tail dependence is important in finance, insurance, and risk management. This paper proposes two tail dependence matrices based on classic rank correlation coefficients,which possess the desired ...Measuring and testing tail dependence is important in finance, insurance, and risk management. This paper proposes two tail dependence matrices based on classic rank correlation coefficients,which possess the desired population properties and interpretability. Their nonparametric estimators with strong consistency and asymptotic distributions are derived using the limit theory of U-processes.The simulation and application studies show that, compared to the tail dependence matrix based on Spearman's ρ with large deviation, the Kendall-based tail dependence measure has stable variances under different tail conditions;thus, it is an effective approach to testing and quantifying tail dependence between random variables.展开更多
基金Supported by the State Key Program of the National Natural Science Foundation of China (Grant No. 12031016)National Natural Science Foundation of China (Grant Nos. 11971324, 11901406, 12201435)+4 种基金Beijing Postdoctoral Research Foundation (Grant No. 2022-ZZ-084)Dalian High-level Talent Innovation Project (Grant No.2020RD09)the Interdisciplinary Construction of Bioinformatics and Statisticsthe Academy for Multidisciplinary StudiesCapital Normal University
文摘Measuring and testing tail dependence is important in finance, insurance, and risk management. This paper proposes two tail dependence matrices based on classic rank correlation coefficients,which possess the desired population properties and interpretability. Their nonparametric estimators with strong consistency and asymptotic distributions are derived using the limit theory of U-processes.The simulation and application studies show that, compared to the tail dependence matrix based on Spearman's ρ with large deviation, the Kendall-based tail dependence measure has stable variances under different tail conditions;thus, it is an effective approach to testing and quantifying tail dependence between random variables.
