期刊
JOURNAL OF ECONOMETRICS
卷 201, 期 2, 页码 384-399出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2017.08.015
关键词
High-dimensional data; High-frequency data; Latent factor model; Principal components; Portfolio optimization
资金
- Fama-Miller Center for Research in Finance
- IBM Faculty Scholar Fund at the University of Chicago Booth School of Business
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.
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