Working Paper: NBER ID: w21584
Authors: Yacine Atsahalia; Dacheng Xiu
Abstract: 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. The explanatory power of the high frequency principal components varies over time. 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.
Keywords: Ito; semimartingale; high frequency; spectral function; eigenvalue; eigenvector; principal components; three factor model
JEL Codes: C22; C55; C58; G01
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| High-frequency data (C58) | Estimation of principal components (C13) |
| High-frequency data (C58) | First principal component (C29) |
| Dynamics of financial sector stocks (G19) | Second principal component (C38) |
| High-frequency analysis (C58) | Systemic risk characteristics (E44) |