Forecasting comparison of long term component dynamic models for realized covariance matrices

Novel model specifications that include a time-varying long-run component in the dy- namics of realized covariance matrices are proposed. The modeling framework allows the secular component to enter the model either additively or as a multiplicative factor, and to be specified parametrically, using a MIDAS filter, or non-parametrically. Estimation is performed by maximizing a Wishart quasi-likelihood function. The one-step ahead forecasting performance is assessed by means of three approaches: model confidence sets, minimum variance portfolios and Value-at-Risk. The results illustrate that the proposed models outperform benchmarks incorporating a constant long-run component, both in and out-of-sample