A Factor-Based Estimation of Integrated Covariance Matrix With Noisy High-Frequency Data

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.

本文研究了一类带有稀疏特质协方差矩阵的连续时间高维因子模型，采用受微观结构噪声污染的异步高频金融数据展开分析。本文聚焦于基于Varneskov提出的平顶已实现核（flat-top realized kernels），对公共因子个数、积分协方差矩阵及其逆开展一致估计。模拟结果表明，所提估计量在有限样本下表现优异。我们将所提方法应用于沪深交易所上市的大量股票的高频价格数据，证实了其在捕捉时变协动关系与资产组合配置中的应用价值。