Geopolitical events impact volatilities of most assets, sectors, and countries. To this end measuring geopolitical risks has been of key interest in the literature as seen by the development of the COVOL index of Engle and Campos-Martins (2023). COVOL uses volatility innovations in multivariate equity index portfolios to quantify global risks. This study looks at how geopolitical risks influence the financial sector. In particular, we use the Diebold-Yilmaz approach to analyse how global uncertainty influences financial stress in the UK and Hungary. We also use a growth-at-risk framework to better understand what part of the growth distribution is impacted by global uncertainty. Finally, using stochastic differential equations we predict the global financial risk index on one month horizon and use machine learning methods to update the model parameters. The purpose of the prediction is to measure the vulnerability of growth in timely manner and to generate stress-test scenarios that account for global uncertainty explicitly.