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Large-dimensional factor modeling based on high-frequency observations
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DOI：10.1016/j.jeconom.2018.09.004.png)
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This paper develops a statistical theory to estimate an unknown factor structure based on financial high-frequency data. We derive an estimator for the number of factors and consistent and asymptotically mixed-normal estimators of the loadings and factors under the assumption of a large number of cross-sectional and high-frequency observations. The estimation approach can separate factors for continuous and rare jump risk. The estimators for the loadings and factors are based on the principal component analysis of the quadratic covariation matrix. The estimator for the number of factors uses a perturbed eigenvalue ratio statistic. In an empirical analysis of the S&P 500 firms we estimate four stable continuous systematic factors, which can be approximated very well by a market and industry portfolios. Jump factors are different from the continuous factors. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Systematic risk
High-dimensional data
High-frequency data
Latent factor model
PCA
Jumps
Semimartingales
Approximate factor model
Number of factors
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