Principal Component Analysis of High Frequency Data / Yacine Aït-Sahalia, Dacheng Xiu.

Format:

Book

Author/Creator:

Aït-Sahalia, Yacine.

Contributor:

National Bureau of Economic Research.

Xiu, Dacheng.

Series:

Working Paper Series (National Bureau of Economic Research) no. w21584.

NBER working paper series no. w21584

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2015.

Summary:

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.

Notes:

Print version record

September 2015.