相依时序逐日盯市条件尾期望的经验估计  

Empirical Estimation on Mark to Market Conditional Tail Expectation underα-mixing Dependence Structure

作　　者：陈皓钰 彭作祥[1] CHEN Haoyu;PENG Zuoxiang(School of Mathematics and Statistics,Southwest University,Chongqing 400715,China)

机构地区：[1]西南大学数学与统计学院,重庆400715

出　　处：《西南师范大学学报（自然科学版）》2023年第5期30-36,共7页Journal of Southwest China Normal University(Natural Science Edition)

摘　　要：Chen等提出了逐日盯市在险价值,并在严平稳ρ混合相依情形下,证明了其经验估计量的大样本性质.基于逐日盯市在险价值,定义了逐日盯市条件尾期望,并在时序满足严平稳强混合条件时,分别证明了经验估计量的强相合性及渐近正态性.Chen et al.(2018)proposed the mark to market value at risk,and proved the large sample properties of its empirical estimator when the time series satisfies the strictly stationaryρ-mixing condition.Based on the mark to market value at risk,this paper defines the mark to market conditional tail expectation,and proves the strong consistency as well as the asymptotic normality of their empirical estimators respectively on the premise of strictly stationaryα-mixing sequence.

关 键 词：逐日盯市条件尾期望 强相合性 渐近正态性 经验估计 强混合 

分 类 号：O211.4[理学—概率论与数理统计]