【主讲人】李少然 助理教授
【主持人】高明 副教授
【报告题目】Augment Large Covariance Matrix Estimation with Auxiliary Network Information

【报告摘要】This paper aims to incorporate auxiliary information about the location of significant correlations into the estimation of high-dimensional covariance matrices. With the development of machine learning techniques such as textual analysis, granular linkage information among firms that used to be notoriously hard to get are now becoming available to researchers. Our proposed method provides an avenue for combining those auxiliary network information with traditional economic datasets to improve the estimation of a large covariance matrix. Simulation results show that the proposed adaptive correlation thresholding method generally performs better in the estimation of covariance matrices than previous methods, especially when the true covariance matrix is sparse and the auxiliary network contains genuine information. As a preliminary application, we apply the method to the estimation of the covariance matrix of asset returns. There are several extensions and improvements that we are considering.

【主讲人介绍】李少然老师于2021年7月获得剑桥大学博士学位，同年加入北京大学经济学院金融系担任助理教授。他的研究方向是金融计量、资产定价、投资组合管理以及机器学习。