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Geopolitics, Geoeconomics, and Sovereign Risk: Different Shocks, Different Channels

Machine Learning 2026-03-25 v7 Machine Learning

Abstract

Geopolitical and geoeconomic shocks reprice sovereign credit risk through different transmission channels. Using a daily panel of 42 advanced and emerging economies over 2018--2025, we show that geopolitical shocks raise sovereign CDS spreads primarily through direct sovereign repricing, while the Global Financial Cycle (GFC) channel moves in the opposite direction and partly offsets that increase -- a ``scissors pattern.'' Geoeconomic shocks, by contrast, transmit mainly through financial conditions, policy uncertainty, and domestic amplification, with only a limited direct repricing component. A semistructural framework provides sign benchmarks for four transmission channels, and a Shapley--Taylor decomposition of nonlinear machine-learning predictions partitions each observation's spread into Direct, GFC, Uncertainty, and Local components. Narrative local projections around four dated crisis events recover the scissors pattern for Russia--Ukraine and support the broader channel taxonomy in the remaining episodes. Additional scorecard, placebo, and sign-restricted SVAR evidence corroborates the taxonomy beyond the baseline ML decomposition. Geopolitical direct effects decay with distance from the conflict zone in a gravity-style pattern (R2 = 0.35 for Russia--Ukraine), while policy-uncertainty shocks activate the Uncertainty channel more globally. The taxonomy implies that liquidity provision can mitigate GFC-driven spread widening, but not direct geopolitical sovereign repricing.

Keywords

Cite

@article{arxiv.2510.12416,
  title  = {Geopolitics, Geoeconomics, and Sovereign Risk: Different Shocks, Different Channels},
  author = {Alvaro Ortiz and Tomasa Rodrigo and Pablo Saborido},
  journal= {arXiv preprint arXiv:2510.12416},
  year   = {2026}
}