报告题目：Clustering Correlation Matrix Models
报 告 人：陈汉
报告时间： 2026年05月25日（周一）15:40-17:00
报告地点：博学楼 I-206
主办单位：高等经济研究院
【报告人简介】
陈汉，湖南大学助理教授，2021年于新加坡管理大学获经济学博士学位。主要研究方向为金融计量经济学与贝叶斯计量经济学，论文曾发表于Journal of Econometrics 等期刊。
【内容摘要】
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.
【更多信息】
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撰稿：王杰 审核：崔惠玉 单位：高等经济研究院
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