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Using principal component analysis to estimate a high dimensional factor model with high-frequency data
- Aït-Sahalia, Yacine, Xiu, Dacheng
- Journal of econometrics 2017 v.201 no.2 pp. 384-399
- econometric models, economic analysis, economic theory, principal component analysis, variance covariance matrix, United States
- 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.