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Large-Dimensional Factor Modeling Based on High-Frequency Observations

Pelger, Markus, Large-Dimensional Factor Modeling Based on High-Frequency Observations (March 17, 2015). Available for download at SSRN: http://ssrn.com/abstract=2584172

“This paper develops an inferential theory for factor models of large dimensions based on high-frequency observations. We derive a new estimator for the number of factors and derive consistent and asymptotically 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 normal “continuous” and rare “tail” risk. The estimator for the loadings and factors is based on the principal component analysis of the quadratic covariation matrix. The estimator for the number of factors uses a perturbed eigenvalue ratio statistic. The results are obtained under general conditions, that allow for a very rich class of stochastic processes and for serial and cross-sectional correlation in the idiosyncratic components. This is the first paper to combine the separate fields of high-frequency econometrics with large dimensional factor analysis.”

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