A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns
提出一种贝叶斯动态线性模型，将微观结构噪声视为测量误差、非同步交易视为缺失数据，通过吉布斯算法估计资产收益的协方差矩阵，模拟和实证表明在流动性差异大、噪声高时优于现有方法。
A multivariate positive definite estimator of the integrated covariance matrix of noisy and asynchronously observed asset returns is proposed. We adopt a Bayesian Dynamic Linear Model where microstructure noise is interpreted as measurement error, and asynchronous trading as missing observations in an otherwise synchronous series. Missing observations are treated as any other parameter, as typical in a Bayesian framework. An augmented Gibbs algorithm is used since all full conditionals are available and its convergence and robustness are discussed. A realistic simulation study compares our estimator with existing alternatives, under different liquidity and microstructure noise conditions. The results suggest that our estimator is superior in terms of RMSE particularly under severe conditions, such as portfolios of assets with heterogeneous liquidity and high level of microstructure noise. The application to the empirical dataset of ten tick-by-tick stock price series confirms the simulation results.