A simple realized factor-based portfolio: improving minimum variance portfolio performance by incorporating low-frequency betas
提出一种混合频率因子协方差估计方法（MFACE），结合高频日内数据和低频日度贝塔，构建高维最小方差投资组合，在降低风险的同时保持较低成本。
Accurate estimation of large covariance and precision matrices is an essential prerequisite of portfolio selection and financial risk management. Among factor-based covariance estimators, high-frequency data provides additional information but also introduces noise, which could degrade the estimation of betas. In this paper, we propose a new Mixed-frequency and FActor-based (MFACE) combining high-frequency (intraday) data and low-frequency (daily) betas. We apply approximate factor structure to construct the high-dimensional minimum variance portfolio. We establish the consistency and obtain the convergence rate of the covariance estimator and the corresponding precision matrix estimator. A comprehensive simulation study investigates the estimation accuracy under different combinations of intrady sampling frequency, intraday sample size and daily sample size. Out-of-sample forecasts demonstrate that our estimator explains the daily volatility of the equally-weighted portfolio pretty well. The minimum variance portfolio that embodies low-frequency betas also achieves the minimum risk at relatively low cost among several estimators.