Using principal component analysis to estimate a high dimensional factor model with high-frequency data
Yacine Ait-Sahalia and
Journal of Econometrics, 2017, vol. 201, issue 2, 384-399
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.
Keywords: High-dimensional data; High-frequency data; Latent factor model; Principal components; Portfolio optimization (search for similar items in EconPapers)
JEL-codes: C13 C14 C55 C58 G01 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:201:y:2017:i:2:p:384-399
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