TradeWarNet: A Causal Attention-based Graph Framework for Analysing Trade War Shocks in Financial Markets

In the prevailing global economic environment, renewed trade tensions, selective tariff measures, and strategic trade interventions have significantly influenced financial market dynamics. These trade war-related shocks generate complex, time-varying, and cross-market effects that are difficult to capture using traditional econometric and correlation-based machine learning approaches. In this paper, TradeWarNet, a causal and attention-based graph learning framework designed to quantify and interpret the impact of trade war shocks on global financial markets is proposed. The proposed framework integrates causal event modelling to isolate trade-induced effects from broader macroeconomic influences and employs a temporal graph attention network to capture dynamic shock transmission across equity, foreign exchange, and commodity markets. Empirical analysis using multi-asset data from both developed and emerging economies demonstrates that TradeWarNet achieves improved volatility forecasting performance, enhanced structural break detection, and greater interpretability compared to benchmark models. The results further indicate that emerging markets exhibit higher sensitivity to trade war shocks under current market conditions, while select safe-haven assets display stabilizing characteristics. The proposed framework offers a policy-relevant and interpretable machine learning approach for analyzing trade-related financial risks.