Exchange Rate Jumps and Geopolitical Risks
Bibliographic record
Abstract
We study the causal relation as well as the Granger-causality and causality-in-quantiles of geopolitical risks in foreign exchange (FX) price jumps, for the period that spans from July 1, 2003, to August 28, 2015. Extended series of different currencies and quantiles are investigated considering seven exchange rates (i) Australian Dollar (AUD), (ii) British Pound (GBP), (iii) Euro (EUR), (iv) New Zealand Dollar (NZD), (v) Canadian Dollar (CAD), (vi) Japanese Yen (JPY) and (vii) Swiss Franc (CHF). We show that geopolitical risks (GPRs) help to predict FX jumps as our results demonstrate a statistically significant and of considerable magnitude relation between geopolitical risks and jumps in the foreign exchange market. The acts of geopolitical risk more severely cause FX total, upside and downside jumps; with the threats of geopolitical risk second, and the geopolitical risk last. In the highest quantiles, NZD is the currency with the highest causalities between geopolitical risks and FX jumps; the highest and lowest causalities are for geopolitical risk (GPR) and geopolitical acts (GPA), respectively. Moreover, the GPR has the highest dispersion of causalities for all FX jumps.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".