Clustering Correlation Matrix Models

15:40-17:00, Monday, May 25, 2026

I-206, Boxue Building

Dr. Han CHEN is an Assistant Professor at Hunan University. He received his Ph.D. in Economics from Singapore Management University in 2021. His main research areas are financial econometrics and Bayesian econometrics. His papers have been published in journals including the Journal of Econometrics.

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.

For more information of the seminar, scan the following QR code(s) to join Tencent QQ group (904 544 292) or WeChat group named "IAER Seminar (4)", please.

QQ Group

WeChat Group 

(QR code is valid until May 26, 2026)