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Using principal component analysis to estimate a high dimensional factor model with high-frequency data
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DOI：10.1016/j.jeconom.2017.08.015.png)
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Abstract
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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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
High-dimensional data
High-frequency data
Latent factor model
Principal components
Portfolio optimization
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