Does Geopolitical Risk Matter for Sovereign Credit Risk? Fresh Evidence from Nonlinear Analysis
Bibliographic record
Abstract
The recent geopolitical uncertainty and the alarming increase in the sovereign credit risk of many countries have motivated us to investigate the potential asymmetric co-movement between geopolitical risk and sovereign credit risk for nineteen countries (China, Russia, USA, Brazil, UK, South Korea, Mexico, Saudi Arabia, Turkey, Sweden, Spain, Norway, Italy, Morocco, France, Bahrain, Abu Dhabi, Japan, and Greece). Using data consisting of Sovereign Credit Default Swap (SCDS), Geopolitical Risk (GPR), and the Quantile-on-Quantile approach (QQA), empirical findings indicate that (i) the effects of GPR on SCDS were heterogeneous, mainly positive, asymmetric, and varied across quantiles and countries; (ii) when the SCDS and GPR are both in upper quantiles, the impacts of GPR are more pronounced; (iii) the countries with the most significant sovereign wealth funds (Norway, China, Saudi Arabia) are less affected by geopolitical uncertainty.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".