How Does the Mauritanian Exchange Rate React During a Crisis? The Case of COVID-19
Bibliographic record
Abstract
This paper examines the impact of the COVID-19 pandemic on the volatility of the EUR/MRU and USD/MRU exchange rates using GARCH-type models. Symmetric GARCH(1,1) and asymmetric specifications—EGARCH and GJR-GARCH—are applied to capture potential leverage effects over two periods: pre-COVID (January 2017–December 2019) and COVID (January 2017–December 2021). The results indicate that the pandemic increased short-run volatility for EUR/MRU, while its impact on USD/MRU was comparatively weaker. Asymmetric models reveal that COVID-19 altered the response of volatility to shocks, with EUR/MRU exhibiting heightened sensitivity and USD/MRU showing contrasting asymmetries. In addition, an out-of-sample backtesting exercise confirms the superior predictive performance of asymmetric models, particularly EGARCH for EUR/MRU and GJR-GARCH for USD/MRU. These findings underscore distinct volatility dynamics and the transmission of external shocks in a small open economy during periods of global 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".