Hierarchical PCA and Modeling Asset Correlations

39 Pages Posted: 2 Sep 2021 Last revised: 3 May 2023

See all articles by Juan A. Serur

Juan A. Serur

NYU - Courant Institute of Mathematical Sciences

Marco Avellaneda

New York University (NYU) - Courant Institute of Mathematical Sciences; Finance Concepts LLC

Date Written: December 1, 2020

Abstract

Modeling cross-sectional correlations between thousands of stocks, across countries and industries, can be challenging. In this paper, we demonstrate the advantages of using Hierarchical Principal Component Analysis (HPCA) over the classic PCA. We also introduce a statistical clustering algorithm to identify homogeneous clusters of stocks or “synthetic sectors”. We apply these methods to study the cross-sectional correlations in the US, Europe, China, and Emerging Markets.

Keywords: correlations, factor models, hierarchical PCA, statistical clusters

JEL Classification: C31, C32, C58, C01, C61

Suggested Citation

Serur, Juan Andrés and Avellaneda, Marco, Hierarchical PCA and Modeling Asset Correlations (December 1, 2020). Available at SSRN: https://ssrn.com/abstract=3903460 or http://dx.doi.org/10.2139/ssrn.3903460

Juan Andrés Serur (Contact Author)

NYU - Courant Institute of Mathematical Sciences ( email )

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Marco Avellaneda

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