分享人：陈汉

地点：经管北楼106

主办方：经济与管理学院

开始时间：2026年5月19日上午9:00

结束时间：2026年5月19日上午9:30

学习交流主题：Clustering Correlation Matrix Models

交流内容简介：Block correlation models have emerged as powerful tools for analyzing dependence in high-dimensional financial time series. Predetermined group assignments have recently been used to define block structures, but these approaches can suffer from statistical inefficiency. This paper introduces a novel block correlation matrix specification and employs an efficient likelihood-based k-means algorithm to estimate the underlying block structure. We demonstrate that both the optimal number of groups and the group memberships are consistently estimated. Furthermore, we establish the asymptotic distribution of the estimated correlations. Simulation studies reveal the strong performance of the proposed method in finite samples. Applying this method to U.S. stock return data, we find it outperforms existing block-forming techniques.

分享人简介：陈汉，湖南大学助理教授，2021年获新加坡管理大学经济学博士学位，主要从事金融计量经济学及贝叶斯计量经济学研究，其相关论文发表于Journal of Econometrics等期刊。