Realized Mixed-frequency Factor Models for Vast Dimensional Covariance Estimation



We introduce a Mixed-Frequency Factor Model (MFFM) to estimate vast-dimensional covariance matrices of asset returns using high-frequency (intraday) data to estimate factor covariances and idiosyncratic risk and low-frequency (daily) data to estimate the factor loadings. Specifically, we propose the use of highly liquid assets such as exchange traded funds (ETFs) as factors. Prices for these contracts are observed essentially free of microstructure noise at high frequencies, allowing us to obtain precise estimates of the factor covariances. The factor loadings instead are estimated from daily data to avoid biases due to market microstructure effects such as the relative illiquidity of individual stocks and non-synchronicity between the returns on factors and stocks. Our theoretical, simulation and empirical results illustrate that the performance of the MFFM is excellent, both compared to conventional factor models based solely on low-frequency data and to realized covariance estimators based on high-frequency data.
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Sebastian Gryglewicz