报告题目：Dynamic modeling via autoregressive conditional GB2 for cross-sectional maxima of financial time series data

时间：2024年11月26日（星期二）上午10:00-12:00

地点：沙河校区，学院1号楼102会议室

报告人：张正军，中国科学院大学经济与管理学院，教授

摘要：This paper introduces the `autoregressive conditional generalized beta distribution of the second kind (AcGB2) to model the dynamic cross-sectional maxima of multivariate financial times series. The temporal dependence of the resulting univariate maxima time series is characterized by the parameter dynamics of the standard GB2 distribution, which offer versatility in approximating various distributions, including heavy-tailed distributions, through parameter adjustments. Consequently, the newly proposed AcGB2 modeling enhances flexibility in fitting real data, especially in scenarios where extreme value theoryconditions are not met, as demonstrated through simulations. Real data analysis is conducted on three datasets: two with medium-sized cross-sectional dimensions ($30$ or fewer) and one with high dimension ($100$ or higher). These datasets are based on the daily negative simple-returns of $30$ stocks in the Dow Jones Industrial Average, stocks in S&P 100, and stocks from 22 primary dealers, respectively. The paper establishes stationary and ergodic solutions for this new time series model under mild parameter conditions and derives the consistency, asymptotic normality, and uniqueness of the statistical conditional maximum likelihood estimators. Joint work with Ning Fan and Chunming Zhang.

报告人简介：报告人张正军教授现为中国科学院大学经济与管理学院长聘教授，国际数理统计协会执行委员和财务总监（July 2016 -- July 2022），国际数理统计协会会士，美国统计协会会士。现担任JASA，JBES，Statistica Sinica，JDS，EJS等国际期刊副主编。主要研究方向包括计量经济学、金融计量学、计算医学与实践、 极端气候等等。在国际顶级期刊：统计（AoS，JASA，JRSSB）、计量（JoE, EE）、金融（JBES, JBF）、医学（AFM，Vaccines）、气象(ATM)等发表论文上百篇。代表性思想和作品包括商相关系数（QCC、TQCC）、非对称广义相关系数（GMC）、滞后尾部相依系数、最大线性回归、最大逻辑回归、EGB2期权定价公式、盯市在险价值（MMVaR）、条件极值Frechet自回归（AcF）等等。

撰稿人：刘洁

审稿人：邓露