Clustering for Block Correlation Models

Han Chen (), Yijie Fei (), Yiren Wang () and Jun Yu ()
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Han Chen: College of Finance and Statistics, Hunan University
Yijie Fei: College of Finance and Statistics, Hunan University
Yiren Wang: College of Finance and Statistics, Hunan University
Jun Yu: Faculty of Business Administration, University of Macau

No 202639, Working Papers from University of Macau, Faculty of Business Administration

Abstract: 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 that it outperforms existing block-forming techniques.

Keywords: Block correlation matrix; Group structure; Generalized Fisher transformation; k-means; Score-driven model (search for similar items in EconPapers)
JEL-codes: C32 C38 C55 C58 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2026-04
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Published in UM-FBA Working Paper Series

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