High‐Frequency‐Based Volatility Model with Network Structure

This paper introduces a novel multi‐variate volatility model that can accommodate appropriately defined network structures based on low‐frequency and high‐frequency data. The model offers substantial reductions in the number of unknown parameters and computational complexity. The model formulation, along with iterative multi‐step‐ahead forecasting and targeting parameterization are discussed. Quasi‐likelihood functions for parameter estimation are proposed and their asymptotic properties are established. A series of simulation studies are carried out to assess the performance of parameter estimation in finite samples. Furthermore, a real data analysis demonstrates that the proposed model outperforms the existing volatility models in prediction of future variances of daily return and realized measures.