Using principal component analysis to estimate a high dimensional factor model with high-frequency data

作者：Ait Sahalia Yacine^*; Xiu Dacheng

来源：Journal of Econometrics, 2017, 201(2): 384-399.

DOI：10.1016/j.jeconom.2017.08.015

• High-dimensional data
• High-frequency data
• Latent factor model
• Principal components
• Portfolio optimization

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.

• 出版日期2017-12