Dynamic principal component CAW models for high-dimensional realized covariance matrices
提出一种动态主成分CAW模型，用于高维资产收益已实现协方差矩阵的建模与预测，通过谱分解和独立动态假设，使模型适用于多达100个资产，并展现出优于竞争模型的预测性能。
We propose a new dynamic principal component CAW model (DPC-CAW) for time-series of high-dimensional realized covariance matrices of asset returns (up to 100 assets). The model performs a spectral decomposition of the scale matrix of a central Wishart distribution and assumes independent dynamics for the principal components' variances and the eigenvector processes. A three-step estimation procedure makes the model applicable to high-dimensional covariance matrices. We analyze the finite sample properties of the estimation approach and provide an empirical application to realized covariance matrices for 100 assets. The DPC-CAW model has particularly good forecasting properties and outperforms its competitors for realized covariance matrices.