High‐Frequency‐Based Volatility Model with Network Structure
提出一种能利用低频和高频数据中的网络结构来预测波动率的新模型，大幅减少参数数量并提升预测精度，对金融风险管理和资产定价有参考价值。
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.