Realized networks
提出一种LASSO型正则化方法，对高频对数价格的大维实现协方差估计进行收缩，得到正定且逆稀疏的协方差矩阵，等价于检测日内对数价格的偏相关网络结构，并证明了估计量的一致性和选择性质。
Summary We introduce LASSO‐type regularization for large‐dimensional realized covariance estimators of log‐prices. The procedure consists of shrinking the off‐diagonal entries of the inverse realized covariance matrix towards zero. This technique produces covariance estimators that are positive definite and with a sparse inverse. We name the estimator realized network, since estimating a sparse inverse realized covariance matrix is equivalent to detecting the partial correlation network structure of the daily log‐prices. The large sample consistency and selection properties of the estimator are established. An application to a panel of US blue chip stocks shows the advantages of the estimator for out‐of‐sample GMV asset allocation.