A blocking and regularization approach to high‐dimensional realized covariance estimation
提出一种区块化与正则化方法，利用高频数据估计高维协方差矩阵，先按流动性分组资产，再用多元已实现核估计分块估计并正则化，模拟和实证表明该方法在不同流动性、噪声信号比和维度下均有效。
SUMMARY We introduce a blocking and regularization approach to estimate high‐dimensional covariances using high‐frequency data. Assets are first grouped according to liquidity. Using the multivariate realized kernel estimator of Barndorff‐Nielsen et al. (2010), the covariance matrix is estimated block‐wise and then regularized. The performance of the resulting blocking and regularization (‘RnB’) estimator is analyzed in an extensive simulation study mimicking the liquidity and market microstructure features of the S&P 1500 universe. The RnB estimator yields efficiency gains for varying liquidity settings, noise‐to‐signal ratios and dimensions. An empirical application of estimating daily covariances of the S&P 500 index confirms the simulation results. Copyright © 2010 John Wiley & Sons, Ltd.