Hierarchical PCA and Modeling Asset Correlations
39 Pages Posted: 2 Sep 2021
Date Written: December 1, 2020
Modeling cross-sectional correlations between thousands of stocks, acrosscountries and industries, can be challenging. In this paper, we demonstratethe advantages of using Hierarchical Principal Component Analysis (HPCA)over the classic PCA. We also introduce a statistical clustering algorithmto identify homogeneous clusters of stocks or “synthetic sectors”. We applythese methods to study 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: 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
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