必威、所2024年系列学术活动（第048场）：朱柯 副教授 香港大学
报告题目：Matrix GARCH Model: Inference and Application
报 告 人：朱柯 副教授 香港大学
报告时间：2024年5月20日 9:00-10:00
报告地点： #腾讯会议：192-671-313
校内联系人：朱复康 fzhu@jlu.edu.cn
报告摘要：Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARCH specification for the trace of conditional row or column covariance matrix, which allows for the model identification. Moreover, we introduce a quasi-maximum likelihood estimator (QMLE) for model estimation and develop a portmanteau test for model diagnostic checking. Simulation studies are conducted to assess the finite-sample performance of the QMLE and portmanteau test. To handle large dimensional matrix time series, we also propose a matrix factor GARCH model. Finally, we demonstrate the superiority of the matrix GARCH and matrix factor GARCH models over existing multivariate GARCH-type models in volatility forecasting and portfolio allocations using three applications on credit default swap prices, global stock sector indices, and future prices.
报告人简介：朱柯博士2011年获得香港科技大学统计学博士学位，同年进入中国科学院数学与系统科学研究院从事研究工作，历任助理研究员、副研究员。2016年加入香港大学任助理教授、副教授。朱柯博士的研究兴趣包括时间序列分析、计量经济、金融大数据等领域。他是International Statistical Institute和Journal of Econometrics的会员。
