We introduce a Mixed-Frequency Factor Model (MFFM) to estimate vast dimensional covari- ance matrices of asset returns. The MFFM uses high-frequency (intraday) data to estimate factor (co)variances and idiosyncratic risk and low-frequency (daily) data to estimate the factor loadings. 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 popular realized covariance estimators based on high-frequency data.

factor models, high-frequency data, microstructure noise, non-synchronous trading, realized covariance
Portfolio Choice; Investment Decisions (jel G11), Corporate Finance and Governance (jel G3)
Erasmus Research Institute of Management
ERIM Report Series Research in Management
Erasmus Research Institute of Management

Bannouh, K, Martens, M.P.E, Oomen, R.C.A, & van Dijk, D.J.C. (2012). Realized mixed-frequency factor models for vast dimensional covariance estimation (No. ERS-2012-017-F&A). ERIM Report Series Research in Management. Erasmus Research Institute of Management. Retrieved from http://hdl.handle.net/1765/37470