Li, Yu-Ning orcid.org/0000-0003-1473-0146, Li, Degui orcid.org/0000-0001-6802-308X and Fryzlewicz, Piotr (2023) Detection of Multiple Structural Breaks in Large Covariance Matrices. Journal of Business and Economic Statistics. pp. 846-861. ISSN 0735-0015
Abstract
This paper studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms ofWild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte-Carlo simulation studies are conducted to examine the finite sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An R package “BSCOV” is provided to implement the proposed algorithms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s) |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Mathematics (York) The University of York > Faculty of Social Sciences (York) > Economics and Related Studies (York) |
Funding Information: | Funder Grant number THE BRITISH ACADEMY SRG1920\100603 |
Depositing User: | Pure (York) |
Date Deposited: | 10 May 2022 14:00 |
Last Modified: | 10 Jan 2025 00:08 |
Published Version: | https://doi.org/10.1080/07350015.2022.2076686 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1080/07350015.2022.2076686 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186612 |
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Description: Detection of Multiple Structural Breaks in Large Covariance Matrices
Licence: CC-BY 2.5