论文标题
分层PCA和建模资产相关性
Hierarchical PCA and Modeling Asset Correlations
论文作者
论文摘要
建模在国家和行业之间成千上万股票之间的横断面相关性可能具有挑战性。在本文中，我们证明了使用分层主成分分析（HPCA）比经典PCA的优点。我们还引入了一种统计聚类算法，用于识别股票的均匀簇或“合成部门”。我们应用这些方法来研究美国，欧洲，中国和新兴市场的横断面相关性。
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.
