Dataset on the Impact of Climate Shocks on US Dollar Volatility Against Major Currencies
Bibliographic record
Abstract
This dataset contains daily and monthly data from 2012 to 2022, capturing exchange rate fluctuations of five major currency pairs: EUR/USD, GBP/USD, USD/CAD, USD/JPY, and USD/CHF. It is designed to analyze the impact of climate-related events on exchange rate volatility. In addition to exchange rate data, the dataset includes detailed records of climate disasters in the USA, Eurozone, U.K., Japan, Canada, and Switzerland. These records comprise total estimated economic damage per event and the corresponding Cumulative Abnormal Returns (CARs) calculated around disaster windows to measure financial market reactions. To provide broader climate context, the dataset also features monthly climate indicators such as average temperature and precipitation changes for each country/region. This comprehensive dataset enables empirical analyses of how climate shocks affect currency markets and can support research on the intersection of environmental risks and financial stability.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 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; both teacher heads agree on what is shown here.
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".