报告题目：Extreme value analytics in financial Big Data

时间：2015年12月30（星期三）13：30-14：30

地点：学院南路校区，学术会堂603

报告人：Professor Zhengjun Zhang, Department of Statistics, University of Wisconsin

报告摘要：

Statistical applications of classical parametric max-stable processes are still sparse mostly due to lack of 1) efficiency of statistical estimation of many parameters in the processes, 2) flexibility of concurrently modeling asymptotic independence and asymptotic dependence among variables, and 3) capability of fitting real data directly. This paper studies a more flexible model, i.e. a class of copula structured M4 (multivariate maxima and moving maxima) processes, and hence CSM4 for short. CSM4 processes are constructed by incorporating sparse random coefficients and structured extreme value copulas in asymptotically (in)dependent M4 (AIM4) processes. As a result, the new model overcomes all of the aforementioned constraints. The paper illustrates these new features and advantages of the CSM4 model using simulated examples and real data of intra-daily maxima of high-frequency financial time series. The paper also studies probabilistic properties of the proposed model, statistical estimators and their properties. (This presentation is based on a joint work with Bin Zhu)

报告人简介：

张正军教授为威尼斯wns885566“手拉手”项目特聘教授，威斯康星大学统计系教授、副主任；北卡罗来纳大学教堂山分校统计学博士，北京航空航天大学管理工程博士。美国统计学会、数理统计学会等多个学会会员，曾获得University of North Carolina教学奖等多项奖励，2010年入选剑桥名人录。主持有10余项美国自然科学基金等科研课题；在JASA等顶级统计学期刊发表学术论文50余篇。同时担任Journal of Business and Economic Statistics等多个国际著名统计学期刊的副主编。