Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data
Rangan Gupta,
Sayar Karmakar,
Christian Pierdzioch
Abstract:We use monthly data covering a century-long sample period (1915–2021) to study whether geopolitical risk helps to forecast subsequent gold volatility. We account not only for geopolitical threats and acts, but also for 39 country-specific sources of geopolitical risk. The response of subsequent volatility is heterogeneous across countries and nonlinear. We find that accounting for geopolitical risk at the country level improves forecast accuracy, especially when we use random forests to estimate our forecastin… Show more
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