时 间：2024 年1月3日 16:00-17:30
地 点：普陀校区理科大楼A 1514
报告人：朱倩倩上海财经大学副教授
主持人：周勇华东师范大学教授
摘 要：
This paper develops a flexible and computationally efficient multivariate volatility model, which allows for dynamic conditional correlations and volatility spillover effects among financial assets. The new model has desirable properties such as identifiability and computational tractability for many assets. A sufficient condition of the strict stationarity is derived for the new process. Two quasi-maximum likelihood estimation methods are proposed for the new model with and without low-rank constraints on the coefficient matrices respectively, and the asymptotic properties for both estimators are established. Moreover, a Bayesian information criterion with selection consistency is developed for order selection, and the testing for volatility spillover effects is carefully discussed. The finite sample performance of the proposed methods is evaluated in simulation studies for small and moderate dimensions. The usefulness of the new model and its inference tools is illustrated by two empirical examples for 5 stock markets and 17 industry portfolios, respectively.
报告人简介：
朱倩倩，上海财经大学统计与管理学院副教授，博士生导师。2017年在香港大学取得博士学位并加入上海财经大学。主要研究方向为时间序列分析，研究成果主要发表在《Journal of the Royal Statistical Society, Series B》、《Journal of Econometrics》、《Econometric Theory》、《Statistica Sinica》及《Journal of Business & Economic Statistics》等国际权威期刊上。主持国家自然科学基金面上项目和青年项目、上海市“浦江人才计划”团队项目和“晨光计划”项目。