Die Veranstaltung findet statt am Dienstag, den 03.11.2020, um 16 Uhr im Rahmen eines Zoom-Meetings. Die Anmeldeinformationen erhalten Sie per Mail.
We propose a dynamic factor state–space model for the prediction of high-dimensional realized covariance matrices of asset returns. Using a Schur decomposition of the joint covariance matrix of assets and factors we express the latent integrated covariance matrix of the individual assets similar to an approximate factor model. We model the individual parts, i.e. the factor and residual covariances as well as the factor loadings, independently via a tractable state-space approach, resulting in closed-form Matrix-F predictive densities for the distinct covariance elements and Student’s t predictive densities for the factor loadings. In an out-of-sample forecasting and portfolio selection exercise we compare the performance of the proposed factor model under different residual dynamics specifications, including block diagonal residuals based on the GICS sector classifications and strict diagonality assumptions as well as combinations of both using linear shrinkage. We find that the proposed model performs very well in an empirical application to realized covariance matrices for 225 NYSE traded stocks using the well-known Fama–French factors and sector-specific factors represented by Exchange Traded Funds (ETFs).
Alle weiteren Termine des Forschungsseminars finden Sie hier.