报告题目：Threshold and hysteretic negative binomial autoregressive models

报告人：朱复康，吉林大学数学学院教授

照片：

邀请人：李本崇

报告时间：2020年8月6日下午15:00--16:00

报告平台：腾讯会议:606 256 515

报告人简介：朱复康，吉林大学数学学院教授、博士生导师，概率统计与数据科学系主任。2008年博士毕业，2013年被破格聘为教授。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 Time Series Analysis等杂志上发表SCI论文40篇，被他人正式引用470余次，单篇文章最高引用110余次。作为负责人获得省部级以上科研项目9项，其中国家自然科学基金4项。现任中国数学会概率统计学会等11个学会的理事或常务理事，美国数学会《数学评论》评论员，已经为JRSSB、JBES等50余个SCI杂志审稿100余次。

报告摘要：Integer-valued time series models have received growing attention in the past three decades, and the integer-valued GARCH models are successful and popular but most existing models assume a linear intensity process. There are many valuable nonlinear phenomena in real life which deserve to be studied, so we propose two classes of negative binomial integer-valued GARCH models with a nonlinear intensity process, which are named as threshold and hysteretic models, respectively. The hysteretic model is a generalization of the threshold one, which enjoys a more flexible regime-switching mechanism. Stability properties of these models are established. The estimation procedure is discussed in detail for the hysteretic model. As an application, we bring attention to some features of the daily number of trades of a stock which have been overlooked in previous studies.