  This paper introduces one new multivariate volatility model that can
accommodate an appropriately defined network structure based on low-frequency
and high-frequency data. The model reduces the number of unknown parameters and
the computational complexity substantially. The model parameterization and
iterative multistep-ahead forecasts are discussed and the targeting
reparameterization is also presented. Quasi-likelihood functions for parameter
estimation are proposed and their asymptotic properties are established. A
series of simulation experiments are carried out to assess the performance of
the estimation in finite samples. An empirical example is demonstrated that the
proposed model outperforms the network GARCH model, with the gains being
particularly significant at short forecast horizons.
