应304am永利集团官网邀请，香港大学张宁宁博士将于2023年12月14日-17日访问兰州大学，并于15日举办专题学术报告，欢迎诸位老师、研究生参加。

报告题目:QUANTILED CONDITIONAL VARIANCE, SKEWNESS, AND KURTOSIS BY CORNISH-FISHER EXPANSION.

时 间：2023年 12月15日（星期五）16：50

地 点：理工楼631

报告摘要：The conditional variance, skewness, and kurtosis play a central role in time series analysis. These three conditional moments (CMs)are often studied by some parametric models but with two big issues: the risk of model mis-specification and the instability of model estimation.To avoid the above two issues, this paper proposes a novel method to estimate these three CMs by the so-called quantiled CMs (QCMs).The QCM method first adopts the idea of Cornish-Fisher expansion to construct a linear regression model, based on n different estimated conditional quantiles. Next, it computes the QCMs simply and simultaneously by using the ordinary least squares estimator ofthis regression model, without any prior estimation of the conditional mean.Under certain conditions, the QCMs are shown to be consistent. Simulation studies indicate that, in the presence of Cornish-Fisher expansion errors and quantile estimation errors caused by the conditional autoregressive value at risk (CAViaR) models, the QCMs perform well under different scenarios.In the application, the study of QCMs for three exchange rates demonstratesthe effectiveness of financial rescue plans during the COVID-19 pandemic outbreak, and suggests that the existing “news impact curve” functions for the conditional skewness and kurtosis may not be suitable. In addition, the backtesting analysis on the value at risk (VaR) indicates that the modified VaR (mVaR) by the QCMs performs better than other two classical VaR models.

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报告人简介

张宁宁博士本科毕业于北京交通大学理学院，之后继续在北京交通大学取得统计学专业的硕士学位，目前在香港大学朱柯教授时间序列课题组攻读博士学位。主要致力于金融时间序列分析和因果推断，目前已在Communications in Nonlinear Science and Numerical Simulation, Physica A: Statistical Mechanics and its Applications, Journal of Business & Economic Statistics, Journal of Agricultural, Biological, and Environmental Statistics等杂志发表多篇文章。

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2023年 12月13日