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
Additional contact information
Sayar Karmakar: University of Florida

Computational Economics, 2024, vol. 64, issue 1, No 18, 487-513

Abstract: 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 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: 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)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-023-10452-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:64:y:2024:i:1:d:10.1007_s10614-023-10452-w

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-023-10452-w

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().