一、报告题目

Predicting the long-term stock market volatility: a GARCH-MIDAS model with variable selection

二、报告人

方彤，中央财经大学统计与数学学院

三、报告时间

12月13日（周四）上午9:30-10:30

四、报告地点

中心校区知新楼B423聚贤报告厅

五、报告人简介

方彤，中央财经大学统计与数学学院数量经济学博士研究生，美国加州大学河滨分校联合培养博士研究生，研究方向为计量经济建模及其在宏观经济和资产定价中的应用。在《中国社会科学》《数量经济技术经济研究》和Economics Letters等杂志发表学术论文十余篇，主持中央财经大学博士研究生重点课题，参与多项国家社科基金重大项目和国家自然科学基金面上项目等。

六、 报告摘要

We consider a GARCH-MIDAS model with the short-term and long-term volatility components, where the long-term volatility component depends on many macroeconomic and financial variables. We select the variables that exhibit the strongest effects on the long-term stock market volatility, via maximizing the penalized log-likelihood function with an Adaptive-Lasso penalty. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model without estimating a large number of parameters. In the empirical analysis, four variables (namely, unemployment level, housing starts, PPI and default spread) are selected among a large set of macroeconomic and financial variables. The post-selection estimation results show negative impacts of unemployment level, housing starts and PPI, and a positive impact of default spread, on the long-term stock market volatility component. The recursive out-of-sample forecasting evaluation shows that the variable selection significantly improves the predictive ability of the GARCH-MIDAS model for the long-term stock market volatility.