Principal Component Analysis of High-Frequency Data

• 成果类型: Article
• 署名作者: Ait-Sahalia, Yacine; Xiu, Dacheng
• 署名单位: Princeton University; National Bureau of Economic Research; University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1401542
• 发表日期: 2019
• 页码: 287-303
• 关键词: dynamic-factor model; asymptotic-distribution; COVARIANCE-MATRIX; long memory; number; inference; eigenstructure; eigenvalues; arbitrage

摘要

We develop the necessary methodology to conduct principal component analysis at high frequency. We construct estimators of realized eigenvalues, eigenvectors, and principal components, and provide the asymptotic distribution of these estimators. Empirically, we study the high-frequency covariance structure of the constituents of the S&P 100 Index using as little as one week of high-frequency data at a time, and examines whether it is compatible with the evidence accumulated over decades of lower frequency returns. We find a surprising consistency between the low- and high-frequency structures. During the recent financial crisis, the first principal component becomes increasingly dominant, explaining up to 60% of the variation on its own, while the second principal component drives the common variation of financial sector stocks. Supplementary materials for this article are available online.