Forecasting Oil Volatility through Network Models with GARCH-Informed Correlation Weights

TL;DR

This paper introduces GARCH-informed network models for oil volatility forecasting, achieving high accuracy with significantly reduced computational costs by leveraging correlation weights derived from multivariate GARCH models.

Contribution

It proposes a novel network topology based on GARCH-informed correlation weights, improving scalability and interpretability in high-dimensional volatility forecasting.

Findings

GARCH-informed network models outperform heuristic-based models in forecasting accuracy.

The proposed models achieve up to 62,000-fold computational savings.

Models match DCC-GARCH predictive performance while enhancing interpretability.

Abstract

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 through a rolling-window exercise using a panel of OPEC members crude oil prices. The results identify network volatility models incorporating these new GARCH-informed weights as the statistically superior specifications. Remarkably, the proposed framework matches standard DCC-GARCH predictive accuracy while delivering up to 62,000-fold computational gains. By explicitly modeling contemporaneous spillovers through interpretable spatial ARCH-like lags estimated via GMM, the proposed approach offers an optimal trade-off between parsimony, interpretability, and performance. The findings establish GARCH-informed network models as robust, scalable alternatives for systemic risk measurement and volatility forecasting in interconnected financial markets.