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Dynamic principal component CAW models for high-dimensional realized covariance matrices

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  • Bastian Gribisch
  • Michael Stollenwerk

Abstract

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.

Suggested Citation

  • Bastian Gribisch & Michael Stollenwerk, 2020. "Dynamic principal component CAW models for high-dimensional realized covariance matrices," Quantitative Finance, Taylor & Francis Journals, vol. 20(5), pages 799-821, May.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:5:p:799-821
    DOI: 10.1080/14697688.2019.1701197
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