Bayesian Nonparametric Covariance Estimation with Noisy and Nonsynchronous Asset Prices

讲座内容简介：

This paper proposes a Bayesian nonparametric approach to estimating the ex-post covariance matrix of asset returns from high-frequency data. Several contributions are made. First, pooling is used to group returns with similar covariance matrices to improve estimation accuracy. Second, a new synchronization method of observations based on data augmentation is introduced. Third, the estimator is guaranteed to be positive definite. Finally, the new approach delivers exact finite sample inference without relying on asymptotic assumptions. The proposed estimator is made robust to independent microstructure noise and nonsynchronous trading. All of those benefits lead to a more accurate estimator, which is confirmed by Monte Carlo simulation results. In real data applications, the proposed covariance estimator results in better portfolio choice outcomes.

讲座嘉宾简介：

Dr. Liu is an Assistant Professor in Finance from Sobey School of Business at Saint Mary's University, Canada. He obtained his Ph.D. degree from McMaster University, Canada. His research interests include financial econometrics and empirical finance. His work has been published in journals such as Journal of Applied Econometrics.