报告题目： A random coefficient absolute autoregressive model with application to bubble
报 告 人：李东 副教授 清华大学
报告时间：2023年6月20日 9：00-10：00
报告地点：腾讯会议 ID：373776267
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：
Financial time series can feature locally explosive behavior when a bubble is formed. The financial bubble, especially its dynamics, is an intriguing topic that has been attracting longstanding attention. To illustrate the dynamics of the local explosion itself, the paper presents a new time series model, called random coefficient absolute autoregressive model, which is always strictly stationary and geometrically ergodic and can create long swings or persistence observed in many macroeconomic variables. When the parameter
, the model has periodically explosive behaviors and can then be used to portray the bubble dynamics. Further, the quasi-maximum likelihood estimation (QMLE) of our model is considered, and its strong consistency and asymptotic normality are established under minimal assumptions on innovation. A new model diagnostic checking statistic is developed for model fitting adequacy. Four reference rules dating collapses of bubble process are heuristically provided from an empirical perspective. Monte Carlo simulation studies are conducted to assess the performance of the QMLE and reference rules in finite samples. Finally, the usefulness of the model is illustrated by an empirical application to the monthly Hang Seng Index.
报告人简介：李东，清华大学统计学研究中心(长聘)副教授，2010年12月毕业于香港科技大学，2013年9月加入清华大学。主要从事计量经济学、金融计量学、时间序列分析、网络数据与大数据分析、机器学习等方面的研究。在统计学和计量统计学杂志上共发表研究论文40余篇。目前担任中国数学会概率统计分会常务理事等.
