Purpose: In this study, we investigated whether a geopolitical risk index (GRI) was a better predictor of Indian commodity prices than traditional econometric models and whether machine-learning models could have improved forecasting performance relative to conventional econometric approaches.

Methodology: In India, the daily wholesale prices of cotton and turmeric were analyzed using the Caldara and Iacoviello global GRI. Risk transmission was quantified using measures of Granger causality and connectedness analysis. Three machine-learning architectures were estimated in both plain and risk-augmented variants and compared with linear econometric benchmarks. The accuracy of the forecasts was evaluated on a held-out partition and was also tested using a rolling-origin cross-validation approach.

Findings: Geopolitical risk was a significant predictor of turmeric prices, but not cotton prices, which is perhaps understandable given that cotton benefits from government price-support mechanisms. For both commodities, the models based on recurrent neural networks were superior to the gradient boosting and linear benchmark models, and adding a geopolitical risk variable enhanced the accuracy of forecasts in almost all models evaluated. Practical

Implications: There is a potential for the inclusion of geopolitical risk indicators in commodity price monitoring systems in emerging markets, with special emphasis on commodities that trade in open, unregulated markets where the predictive relationship was clearly established.

Originality: The study provided evidence of geopolitical risk transmission to structurally contrasting Indian agricultural commodities using daily-frequency data and compared the forecasting performance of machine-learning and econometric models in the context of risk augmentation.

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