Principal Component Analysis of High-Frequency Data
By Yacine Aït-Sahalia, D. Xiu. Journal of the American Statistical Association, 2016
254 citations recorded by the source corpus.
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, 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.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/0642219f697f6d184dfafc7f85ef8b98292185c3
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Citations in the corpus
- Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019, background): “, 2018), a local window or high frequency approach (Pelger, 2019; Aı̈t-Sahalia and Xiu, 2018) or a kernel projection in the time dimension (Pelger and Xiong, 2018).”
- State-Varying Factor Models of Large Dimensions (2018, methodology): “Pelger (2019a, 2019b) and Aït-Sahalia and Xiu (2019a, 2017b) allow for a large number of changes using high-frequency data. The idea is similar to the local PCA estimators, but the high-frequency data provides sufficient information to detect more rapid changes. However, appropriate high-frequency d”