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Dynamic Principal Component CAW Models for High-Dimensional Realized Covariance Matrices
We propose a new dynamic principal component CAW model (DPC-CAW) for time-series of
high-dimensional realized covariance matrices of asset returns. 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 nite 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.
