Before revision: Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality . Consequently, parsimonious estimation methods are essential for conducting reliable statistical inference. In the paper at hand, this issue is addressed by modeling the underlying co-volatility dynamics of a time series vector through a lower dimensional collection of latent time-varying stochastic factors.
After revision: Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality in dynamic covariance estimation by modeling the underlying co-volatility dynamics of a time series vector through a lower dimensional collection of latent time-varying stochastic factors.