An Integrated Framework for Visualizing and Forecasting Realized Covariance Matrices
An Integrated Framework for Visualizing and Forecasting Realized Covariance Matrices
复制标题
实现协方差矩阵可视化和预测的集成框架
DOI：
10.1007/s42081-020-00100-0
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发表时间：
2020
影响因子：
1.3
通讯作者：
H. Shigemoto and T. Morimoto
中科院分区：
文献类型：
--
作者：
Shigemoto Hideto;Morimoto Takayuki;Hideto Shigemoto and Takayuki Morimoto;H. Shigemoto and T. Morimoto
本文提出了一个集成的框架可视化和预测实现协方差矩阵，使有效的建设和预测的最佳投资组合。多变量实现核通常来自日内高频数据，然后用于通过图形套索算法估计实现协方差矩阵。为了预测已实现的协方差，我们采用条件自回归Wishart模型及其变体。最后，我们计算Stein损失函数，并执行模型置信集程序，以获得最优投资组合选择的最佳模型。
This paper proposes an integrated framework for visualizing and forecasting realized covariance matrices to enable the efficient construction and prediction of an optimal portfolio. Multivariate realized kernels are typically derived from intra-day high-frequency data, and are then used to estimate the realized covariance matrix via the graphical lasso algorithm. To forecast the realized covariances, we employ the conditional autoregressive Wishart model and its variants. Finally, we compute the Stein loss function and execute the model-confidence-set procedure to obtain the best model for optimal portfolio selection.