In previous studies, high-frequency data has been used to improve portfolio allocation by estimating the full realized covariance matrix. In this paper, we show that strategies using high-frequency data for measuring and forecasting univariate realized volatility alone can already generate...

Estimation noise is a well-known issue in empirical portfolio modelling. Estimated weights are known to have huge standard errors and bad predictive quality, which often results in an inferior out-of-sample portfolio performance compared to simple alternatives. Most of the recent literature...

This paper analyzes the forecast accuracy of the multivariate realized volatility model introduced by Chiriac and Voev (2010), subject to different degrees of model parametrization and economic evaluation criteria. By modelling the Cholesky factors of the covariance matrices, the model generates...