Principal Component Analysis of High-Frequency Data
Principal Component Analysis of High-Frequency Data
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DOI：
10.1080/01621459.2017.1401542
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发表时间：
2019-01-02
影响因子：
3.7
通讯作者：
Xiu, Dacheng
中科院分区：
文献类型：
--
作者：
Ait-Sahalia, Yacine;Xiu, Dacheng
我们开发了必要的方法进行高频主成分分析。我们构造实现特征值，特征向量和主成分的估计，并提供这些估计的渐近分布。在实证方面，我们研究了S&P 100指数成分股的高频协方差结构，每次只使用一周的高频数据，并检查它是否与几十年来积累的低频回报的证据相一致。我们发现了一个令人惊讶的一致性之间的低频和高频结构。在最近的金融危机中，第一主成分变得越来越占主导地位，其本身可以解释高达60%的变化，而第二主成分则驱动了金融部门股票的共同变化。本文的补充材料可在网上查阅。
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.