报告题目：Quotient Correlation Coefficients

时 间： 2014年6月9日上午10:00

地 点： 数学学科楼五层报告厅

报 告 人： Prof. Zhengjun Zhang

报告人简介：张正军(Zhengjun Zhang)，现任美国威斯康星大学麦迪逊分校统计系教授，统计系副主任，招生委员会主任。1996年获北京航空航天大学管理学博士学位后，赴美国北卡罗来纳教堂山分校深造，师从世界著名统计学家Richard Smith，2002年获得统计学博士学位。研究领域包括金融华体会（中国）官方序列分析，风险管理，极值理论，贝叶斯统计，医学统计等。担任商业与经济统计学（Journal of Business and Economic Statistics），统计推断（Statistics and Its Interface）等顶级期刊主编；美国国家科学基金会（National Science Foundation）专业委员会成员。曾在统计学年鉴（Annals of Statistics），应用统计学年鉴（Annals of Applied Statistics），美国数理统计学会会刊（Journal of American Statistical Association），华体会（中国）官方序列分析期刊（ Journal of Time Series Analysis）和家庭医学年鉴 （Annals of Family Medicine）等世界顶级学术期刊发表论文近百篇。

报告摘要：Identification of tail dependence among observations is important and challenging, but remains an open problem, due to the fact that tail dependence is primarily captured by values above thresholds. This talk introduces a class of tail quotient correlation coefficients (TQCC) which allows the underlying threshold values to be random. An approximation theorem of conditional tail probabilities is established. The limiting distribution of TQCC under the null hypothesis of tail independence is derived. Test statistics for tail independence are constructed and shown to be consistent under the alternative hypothesis of tail dependence. Our empirical results demonstrate different tail dependence structures underlying various global financial markets. Either omission or unanimous treatment of the tail dependence structures for different financial markets will lead to erroneous conclusions or suboptimal investment choices. Applications of TQCC have the potential to serve as an useful tool in exploiting arbitrage opportunities, optimizing asset allocations, and building robust risk management strategies.

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