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 (Computational Economics, 2023)
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 forecasting models. As an extension, we report empirical evidence on the predictive value of the country-level sources of geopolitical risk for two other candidate safe-haven assets, oil and silver, over the sample periods 1900–2021 and 1915–2021, respectively. Our results have important implications for the portfolio and risk-management decisions of investors who seek a safe haven in times of heightened geopolitical tensions.
Keywords: C22; D80; H56; Q02; 330; Gold; Geopolitical risk; Forecasting; Returns; Volatility; Random forests