Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data

Rangan Gupta, Sayar Karmakar () and Christian Pierdzioch
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Sayar Karmakar: Department of Statistics, University of Florida, 230 Newell Drive, Gainesville, FL, 32601, USA

No 202201, Working Papers from University of Pretoria, Department of Economics

Abstract: We use monthly data covering a century-long sample period (1915-2021) to study whether geopolitical risk helps to forecast subsequent gold returns and 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 returns and 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 decisions of investors who seek a safe haven in times of heightened geopolitical tensions.

Keywords: Gold; Geopolitical Risk; Forecasting; Returns; Volatility; Random Forests (search for similar items in EconPapers)
JEL-codes: C22 D80 H56 Q02 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2022-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-his and nep-rmg
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Journal Article: Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data (2024)
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