Weighted Least Squares Realized Covariation Estimation
提出一种加权最小二乘法，用高频数据估计每日实现协变和微观结构噪声方差，通过蒙特卡洛模拟和道琼斯30成分股数据验证其优于现有方法，并展示噪声方差可用于改进波动率预测和资产配置。
We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.