WOD样本下VaR核估计的强相合性及Bahadur表示  

Strong consistency and Bahadur representation of VaR kernel estimation for WOD samples

作　　者：王巍 朱勇 吴青青 吴燚 WANG Wei;ZHU Yong;WU Qingqing;WU Yi(College of Big Data and Artificial Intelligence, Chizhou University, Chizhou 247000, China)

机构地区：[1]池州学院大数据与人工智能学院,安徽池州247000

出　　处：《湖北大学学报（自然科学版）》2022年第4期423-428,共6页Journal of Hubei University：Natural Science

基　　金：安徽省自然科学基金(2108085QA15);安徽高校自然科学研究重点项目(KJ2018A0579);安徽省高校人文社会科学研究重点项目(SK2021A0739)资助。

摘　　要：风险价值是金融界测量市场风险的主流指标,被众多金融机构所采用,其估计量的研究是一个值得探讨的问题.利用WOD序列的性质及其指数不等式,在WOD样本下研究风险价值核估计量的性质,同时给出风险价值核估计的收敛速率和Bahadur表示,运用数值模拟的方式评估核估计的效果,所获结论推广了相关文献的研究结果.Value-at-Risk(VaR)is the mainstream indicator of measuring market risk in the financial industry,and it is used by many financial institutions.The estimator of VaR is a problem worth discussing.In this article,by utilizing the properties and exponential inequalities of WOD sequence,the properties of the VaR kernel estimator under the WOD samples were discussed.The Bahadur representation and the convergence rate of the VaR kernel estimator were also obtained.Numerical simulation was also used to evaluate the effect of the VaR kernel estimation.The conclusion obtained extended the research results of related literature.

关 键 词：WOD样本 风险价值 核估计 BAHADUR表示 

分 类 号：O211.4[理学—概率论与数理统计] O212.7[理学—数学]