报告题目：Large Covariance Matrix Estimation with Factor-assisted Variable Clustering
报告嘉宾： 李 东 清华大学
报告时间：2024年11月18日(周一) 16:30-18:00
报告地点：中国科学院大学中关村校区教学楼N513
内容摘要
This talk studies the estimation of covariance matrices for high-dimensional time series within a new framework that combines low-rank factor and latent variable-specific clustering structures. The popular methods based on assuming the sparse error covariance matrix after taking out the common factors may be invalid for many financial applications. Our formulation postulates a latent model-based error cluster structure after removing the observable factors, which not only leads to more interpretable clustering patterns but also accounts for the non-sparse cross-sectional correlations among the variable-specific residuals. Our method begins with using least-squares to estimate the factor loadings, followed by identifying the latent cluster structure by thresholding the scaled covariance difference measures of residuals. A novel ratio-based criterion is introduced to determine the threshold parameter when performing the developed clustering algorithm. We then establish the clustering recovery consistency of our method and derive the rates of convergence for our proposed covariance matrix estimators under different norms. Finally, we demonstrate the superior sample performance of our proposal over the competing methods through both extensive simulations and a real data application on minimum variance portfolio.
主讲人简介
李东，清华大学统计与数据科学系副教授， 2013年9月加入清华大学，主要从事复杂时间序列的统计分析、非欧数据分析、网络数据分析、机器学习、金融计量学等方面的研究。
