Graph Neural Networks and Explainable Spillovers: Global Monetary and Oil Shocks in GCC Financial Markets

This study investigates how global monetary and oil shocks propagate across advanced and pegged oil economies, focusing on the United States, Germany, the United Kingdom, Saudi Arabia, and the United Arab Emirates over the period 2015–2023. It examines which transmission channels—liquidity, credit, or equity—serve as the dominant conduits of spillovers under fixed exchange rate regimes. To address this question, this paper develops a hybrid causal–computational framework that integrates high-frequency identification of monetary and oil shocks with econometric benchmarks (Local Projections and Time-Varying Parameter VARs) and a Graph Neural Network-based Causal Shock Network (GNN-CSN) enhanced with SHAP explainability. The results show that global monetary shocks significantly raise interbank funding costs in Saudi Arabia and the UAE, while sovereign credit spreads remain largely stable, indicating that liquidity—not credit—constitutes the main transmission channel. Equity markets absorb much of the external adjustment, reflecting sectoral sensitivity to global cycles. By combining causal identification, dynamic estimation, and explainable machine learning, the framework improves predictive accuracy and transparency, offering new evidence on how external shocks shape financial dynamics in resource-dependent, dollar-pegged economies.