Hierarchical PCA and Modeling Asset Correlations
Mathematical Finance
2020-10-09 v1
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 for identifying of homogeneous clusters of stocks, or "synthetic sectors". We apply these methods to study cross-sectional correlations in the US, Europe, China, and Emerging Markets.
Keywords
Cite
@article{arxiv.2010.04140,
title = {Hierarchical PCA and Modeling Asset Correlations},
author = {Marco Avellaneda and Juan Andrés Serur},
journal= {arXiv preprint arXiv:2010.04140},
year = {2020}
}