Foreign exchange market response to pandemic-induced fear: Evidence from (a)symmetric wild bootstrap likelihood ratio approach
Bibliographic record
Abstract
This study tested whether pandemic-induced fear is a predictor of the exchange rate returns of seven major currencies – Australian dollar, Canadian dollar, Swiss franc, yuan, EURO, pound sterling, and yen. Daily data on US dollar-based exchange rate returns and the global fear index for COVID-19 pandemic for the period 10-02-2020–02-04-2021 were used. Symmetric and asymmetric wild bootstrap likelihood ratio tests were employed in testing the relationship. The symmetric test results showed that pandemic-induced fear is capable of predicting the exchange rate returns of the Swiss franc, yuan, and the EURO. Specifically, negative relationships were recorded between their returns and the global fear index for pandemics. The asymmetric test results however showed that increasing pandemic-induced fear leads to decreases in the returns of the Australian dollar, Canadian dollar, Swiss franc, yuan and EURO. Overall, this study showed that pandemic-induced fear is a predictor of exchange rate returns. It is therefore suggested that the maintenance of stability in the financial system should be treated as an integral part of policy responses designed to mitigate the adverse effects of pandemics. This way, economic agents will not be forced to move their investments to foreign currency-denominated assets due to fear of investment losses.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".