Equity clusters through the lens of realized semicorrelations

Tim Bollerslev, Andrew Patton and Haozhe Zhang

Economics Letters, 2022, vol. 211, issue C

Abstract: We rely on newly-developed realized semicorrelations constructed from high-frequency returns together with hierarchical clustering and cross-validation techniques to identify groups of individual stocks that share common features. Implementing the new procedures based on intraday data for the S&P 100 constituents spanning 2019-2020, we uncover distinct changes in the “optimal” groupings of the stocks coincident with the onset of the COVID-19 pandemic. Many of the clusters estimated with data post-January 2020 evidence clear differences from conventional industry type classifications. They also differ from the clusters estimated with standard realized correlations, underscoring the advantages of “looking inside” the correlation matrix through the lens of the new realized semicorrelations.

Keywords: Clustering; Stock returns; High-frequency data; Semicorrelations; COVID-19 (search for similar items in EconPapers)
JEL-codes: C32 C38 C58 G10 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:211:y:2022:i:c:s016517652100478x

DOI: 10.1016/j.econlet.2021.110245

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