Damásio, Bruno Miguel PintoReis, Vasco Montez Barata Coelho Dos2026-02-162026-02-162026-02-05http://hdl.handle.net/10362/200417Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and ManagementThis study examines the impact of geopolitical risk on the volatility of Brent crude oil, gold and wheat in the period from 2020 and 2025, with particular focus on the Russia–Ukraine war. Understanding how geopolitical tensions influence commodity markets is essential for investors, policymakers and risk managers as these assets play a central role in global energy, financial stability and food security. The analysis focuses on identifying the extent to which geopolitical shocks influence commodity market volatility and on comparing the forecasting performance of econometric models with that of machine learning techniques . Daily price data were collected from Yahoo Finance and combined with the Geopolitical Risk Index, and the empirical analysis relied on GARCH-type volatility models (GARCH, EGARCH and GJR-GARCH), a GARCH–MIDAS specification incorporating the Geopolitical Risk Index as a low-frequency driver, and two non-linear machine learning methods, Random Forest and Gradient Boosting. The results show that increases in geopolitical risk significantly amplify volatility, with econometric models being more effective in capturing short term clustering and machine learning methods providing stronger predictive accuracy out of sample. These findings highlight the value of incorporating geopolitical indicators into risk management frameworks and clarify the channels through which geopolitical tensions shape commodity market dynamics , offering evidence that supports more reliable forecasting in periods of heightened uncertaintyengGeopolitical Risk Index (GPR)Commodity VolatilityGARCH-MIDASMachine LearningRussia–Ukraine WarBrent Crude OilGoldWheatmaster thesis204223350