Forecasting Oil Volatility through Network Models with GARCH-Informed Correlation Weights
基于 GARCH 条件相关权重的网络模型与石油波动率预测
This study addresses the computational challenges of forecasting volatility in high-dimensional commodity markets. Building on the Network log-ARCH framework, we introduce a novel class of network topologies from GARCH-informed correlation weights, obtained from conditional covariance estimates of multivariate GARCH models, rather than relying on the heuristic distance measures commonly used in clustering methods. We evaluate the proposed models forecasting performance…