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

Yacine Ait-Sahalia and Dacheng Xiu

Journal of Econometrics, 2017, vol. 201, issue 2, 384-399

Abstract: 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)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (87)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:201:y:2017:i:2:p:384-399

DOI: 10.1016/j.jeconom.2017.08.015

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