A Factor-Based Estimation of Integrated Covariance Matrix With Noisy High-Frequency Data
针对高频金融数据中的微观结构噪声和异步观测问题，提出一种基于因子模型的集成协方差矩阵及其逆矩阵的一致估计方法，并在沪深股市数据中验证了其在时变协动捕捉和投资组合配置中的价值。
This article studies a high-dimensional factor model with sparse idiosyncratic covariance matrix in continuous time, using asynchronous high-frequency financial data contaminated by microstructure noise. We focus on consistent estimations of the number of common factors, the integrated covariance matrix and its inverse, based on the flat-top realized kernels introduced by Varneskov. Simulation results illustrate the satisfactory performance of our estimators in finite samples. We apply our methodology to the high-frequency price data on a large number of stocks traded in Shanghai and Shenzhen stock exchanges, and demonstrate its value for capturing time-varying covariations and portfolio allocation.