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We introduce a parametric dynamic factor specification for high-frequency financial data that simplifies considerably the estimation of the realized covariance matrix in high dimensions. The estimation method is tested in an empirical setting that emphasizes the effect of the curse of dimensionality. Compared to standard parametric approaches, our factor specification is computationally less demanding and provides statistically indistinguishable performances in standard risk management applications. The method is also assessed on Monte-Carlo simulations under several forms of misspecification.

High-dimensional realized covariance estimation: a parametric approach