报告人：朱复康教授 吉林大学
线上会议：#腾讯会议：186-298-456
会议时间：2022/10/26 16:00-16:45
主持人：夏强教授

报告摘要:The Glosten–Jagannathan–Runkle GARCH (GJR-GARCH) model is popular in accounting for asymmetric responses in the volatility in the analysis of continuous-valued financial time series, but asymmetric responses in the volatility are also observed in time series of counts or ℤ-valued time series, such as the daily number of stock transactions or the daily stock returns divided by tick price (1 cent). Two different integer-valued GARCH models based on Poisson distribution have been proposed for these two types of discrete data respectively. Shifted geometric distribution is more flexible than Poisson distribution, whose variance is greater than its mean. In this article, we propose a GJR-GARCH model based on shifted geometric distribution for ℤ-valued time series exhibiting asymmetric volatility. Basic probabilistic properties of the new model are given, and the maximum likelihood method is used to estimate unknown parameters and the asymptotic normality of corresponding estimators is established. A simulation study is presented to illustrate the estimation method. An empirical application to a real data concerning the daily stock returns divided by tick price is considered to show the proposed model’s superiority compared with
existing models.

报告人简介：朱复康，吉林大学数学学院教授、博士生导师，吉林国家应用数学中心副主任、概率统计与数据科学系主任。2008年博士毕业，2013年破格晋升教授，2021年被聘为“唐敖庆学者”领军教授B岗。主要从事时间序列分析和金融统计的研究，已经在Annals of Applied Statistics、Journal of Business & Economic Statistics、Statistica Sinica等期刊上发表论文50余篇，其中入选ESI前1%高被引论文2篇。主持国家自然科学基金面上项目3项和青年基金1项，曾获得教育部自然科学奖二等奖、吉林省科学技术奖二等奖等科研奖励。现任中国现场统计研究会、中国数学会概率统计学会等学会的理事或常务理事，是JRSSB、JBES、AoAS等70余个SCI期刊的匿名审稿人。

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