Do Geopolitical Shocks Drive Currency Volatility? New Evidence from a TVP-VAR Framework
Bibliographic record
Abstract
Exchange rate dynamics in OECD economies have been increasingly shaped by geopolitical tensions and systemic crises. Between 2010 and 2025, a sequence of major events including the European sovereign debt crisis, the COVID-19 pandemic, and the Russia–Ukraine conflict has amplified uncertainty and volatility in global financial markets. Using a Bayesian Time-Varying Parameter Vector Autoregression (TVP-VAR) model, this analysis investigates how geopolitical shocks are transmitted to exchange rate movements against the US dollar, capturing structural breaks, stochastic volatility, and heterogeneous time-varying relationships across countries. The empirical evidence reveals that exchange rates respond significantly but asymmetrically to geopolitical shocks, with more pronounced effects during periods of global turmoil and weaker reactions in stable phases. Furthermore, the sensitivity of exchange rates to geopolitical risk differs across economies, depending on institutional quality, trade exposure, and macroeconomic resilience. These findings highlight important asymmetries in the transmission of geopolitical uncertainty and underscore the heterogeneity of policy responses among advanced economies. From a practical perspective, the results provide valuable guidance for policymakers and international investors seeking to integrate geopolitical risk into monetary, fiscal, and risk management frameworks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".