Geopolitics, Geoeconomics and Risk:A Machine Learning Approach

This paper studies how geopolitical and geoeconomic shocks transmit to sovereign risk. Using a daily panel of CDS spreads, financial variables, and news-based indicators for 42 advanced and emerging economies over 2018-2025, we estimate nonlinear Machine Learning models that capture the interactions and threshold effects through which these shocks operate. A Shapley-Taylor decomposition exactly partitions predicted spreads into four channels: Direct, Global Financial Cycle, Uncertainty, and Local. The decomposition reveals a structural distinction. Geopolitical shocks enter through the Direct channel -- repricing default probability -- with the global financial cycle providing a transient offset. Geoeconomic uncertainty shocks bypass the Direct channel and operate through expected monetary policy and risk appetite. Gravity regressions show geopolitical transmission decays with distance; policy-uncertainty shocks activate the Uncertainty channel globally. The taxonomy implies that liquidity provision can address financial-cycle-mediated transmission but not the persistent component of geopolitical risk premia.

• Publication year

  2025

• Venue

  arXiv.org

• Publication date

  2025-10-14

• Fields of study

  Mathematics, Computer Science, Economics, Political Science

• Identifiers
  DOI 10.48550/arXiv.2510.12416arXiv 2510.12416
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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