Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data
Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data
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DOI：
10.2139/ssrn.2669506
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Yacine Ait-Sahalia;D. Xiu
中科院分区：
文献类型：
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作者：
Yacine Ait-Sahalia;D. Xiu
本文构建了一个在采样频率和变量数量都增加的情况下公因子数量的估计器。根据经验，我们记录了大型美国股票投资组合的协方差矩阵可以通过具有稀疏残差矩阵的低秩公共结构很好地表示。当用于样本外投资组合分配时，所提出的估计器在很大程度上优于样本协方差估计器。
This paper constructs an estimator for the number of common factors in a setting where both the sampling frequency and the number of variables increase. Empirically, we document that the covariance matrix of a large portfolio of US equities is well represented by a low rank common structure with sparse residual matrix. When employed for out-of-sample portfolio allocation, the proposed estimator largely outperforms the sample covariance estimator.