The Exchange Rate Volatility During Political Protests: Event Study and the Case of Belarus
Bibliographic record
Abstract
The exchange rate reacts on political protests. Market agents affected by unrest increase exchange rate volatility. This may be converted into currency devaluation if monetary authorities decide to join protesters rather than supporting the exchange rate. Based on the event study methodology, three hypotheses were tested on 1,220 event windows of 77 political protests, in 54 economies, in 2017-2022, on three points: (1) the types of political protests with the highest abnormal exchange rate volatility and currency returns; (2) the influence of protests on daily currency devaluation; (3) the effects of unrest on intraday exchange rate volatility. The findings show that the highest exchange rate volatility was in the groups of events with short duration, with a small number of participants, which were non-violent, motivated by electoral fraud, without outcomes, and in partly free countries. The highest currency devaluation was in the groups of unrest with the greatest number of protesters, lasting more than a month, and in free countries. Only rare cases prove a high statistically significant influence of protests on exchange rate volatility and currency devaluation. As the case-by-case approach is preferable, the case of Belarus, and the country’s 14 largest political protests in 2020, was studied. This showed that four-month street unrests affected the abnormal intraday volatility of USD/BYN. After two weeks of protests, market volatility would have led to devaluation, if the National Bank hadn’t intervened, and in two months of unrest, exchange rate volatility started falling.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".