Large dimensional portfolio allocation based on a mixed frequency dynamic factor model

In this paper, we propose a mixed-frequency dynamic factor model (MFDFM) taking into account the high-frequency variation and low-frequency variation at the same time. The factor loadings in our model are affected by the past quadratic variation of factor returns, while the process of the factor quadratic variation is under a mixed-frequency framework (DCC-RV). By combing the variations from the high-frequency and low-frequency domain, our approach exhibits a better estimation and forecast of the assets covariance matrix. Our empirical study compares our MFDFM model with the sample realized covariance matrix and the traditional factor model with intraday returns or daily returns. The results of the empirical study indicate that our proposed model indeed outperforms other models in the sense that the Markowitz's portfolios based on the MFDFM have a better performance.