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- W3125965402 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." @default.
- W3125965402 created "2021-02-01" @default.
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- W3125965402 date "2018-06-28" @default.
- W3125965402 modified "2023-10-17" @default.
- W3125965402 title "Principal Component Analysis of High-Frequency Data" @default.
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- W3125965402 doi "https://doi.org/10.1080/01621459.2017.1401542" @default.
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