Factor State-Space Models for High-Dimensional Realized Covariance Matrices of Asset Returns

We propose a dynamic factor state-space model for high-dimensional covariance matrices of asset returns. It uses observed risk factors and assumes that the latent covariance matrix of assets and factors is observed through their realized covariance matrix with a Wishart measurement density. The imposed strict factor structure allows for dynamics in the covariances of the factors and the residual components as well as in the factor loadings. The model structure facilitates inference using simple Bayesian MCMC procedures making the approach scalable w.r.t. the number of assets. An empirical application shows that the model performs very well in-and-out-of-sample