The impact of us elections on the dollar’s exchange rate
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores the effect of U.S. domestic politics on the behaviour of international currency markets. Specifically, for the first time in the literature, we gauge the impact of a divided government on the exchange rate volatility of five currencies: the Japanese yen, the Canadian dollar, the British pound, the Mexican peso, and the euro. At the same time, we control for the impact of political and macroeconomic factors. A GARCH methodology has been adopted for this objective, using weekly data from 2000 to 2021. The evidence suggests that the partisan and divided government variables significantly impact the conditional variance equation, whilst the observed reduced levels of exchange rate volatility during a Democrat presidency run counter to prior studies on partisanship. In addition, exchange rate volatility seems to increase one month before an election and during periods of divided government. Given the nascent evidence, we argue that U.S. politics are instrumental in affecting global financial markets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.010 |
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 it