The Nexus of COVID-19 Pandemic, Foreign Exchange Rates, and Short-Term Returns
Bibliographic record
Abstract
The current study examines short-term abnormal returns of eight major currencies including EUR/USD, GBP/USD, USD/AUD, USD/CAD, USD/CHF, USD/CNY, USD/JPY, and USD/SEK in response to the evolution of the COVID-19 pandemic using event study approach in three different scenarios. Firstly, short-term abnormal returns of major currencies are estimated on the day of World Health Organization’s (WHO) announcement declaring COVID-19 as a pandemic. Secondly, they are estimated on the day of the announcement of the first confirmed case of COVID-19 in the respective country. Thirdly, they are estimated on the day of the announcement of the first death from COVID-19 in each country. The results provided evidence that major currency investors earned positive returns in these three different scenarios. The implications of the current study are more important than anticipated. Government policymakers, foreign exchange market regulators, and foreign exchange market participants can anticipate short-term returns while establishing foreign exchange policies, designing rules and regulations, and finalizing trading and hedging strategies, respectively, in situations such as the current COVID-19 pandemic. Received Date: September 20, 20202 Last Received: October 23, 2020 Acceptance: November 13, 2020
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".