High-Dimensional Multivariate Realized Volatility Forecasting with Community Network Structure
提出CNHARQ模型，利用相关性随机块模型从资产已实现波动率的关联网络中提取社区结构，将参数从O(N²)降至O(NK)，实证表明基于网络的社区结构比传统行业分类更能提升波动率预测效果。
We introduce the Community Network Heterogeneous Autoregressive model with Quarticity (CNHARQ), a novel framework that integrates network-based information to enhance the forecasting of multivariate realized volatilities. To address the curse of dimensionality, we propose a Correlation-Based Stochastic Block Model (CBSBM) to uncover latent community structures from the correlation network of realized volatilities of N assets. This approach reduces the number of unknown parameters in the model from O(N2) to O(NK), where K≪N denotes the number of communities. Empirical analysis demonstrates that the CBSBM captures dynamic community structures, revealing shifts in the co-movement of asset volatilities over time. Furthermore, the CBSBM-based community structure outperforms the conventional Global Industry Classification Standard (GICS) in out-of-sample volatility forecasting, highlighting the superior forecasting power of network-based correlations over traditional industry classification schemes.