Coronavirus Pandemic Impact on the Nexus Between Gold and Bitcoin Prices
Bibliographic record
Abstract
This study aims to explore the Coronavirus disease (COVID-19) effects on gold and bitcoin prices variabilities and on the relationship between each of them, both prices are denominated in USD.The study period is divided into two groups, first group included 120 workdays before 30 January 2020 when WHO first declared COIVD-19 outbreak as a public health emergency of international concern, and the second group included 120 observations post that date. The period as a total extends from June. 24, 2019 to 22 of May 2020.To this end, the study used the appropriate statistical tools including stationery and unit root test, Levene's test for the equality of variances, correlation, least squares regression, and pairwise Granger causality test.The results of testing the equality of variances and homogeneity between each of the study groups before and after COVID 19 revealed a strong rejection of the null hypothesis of equal variances for gold but not bitcoin which was accepted. The results also indicate a significant relationship between gold and bitcoin before and after COVID-19, but the sign changed from negative to positive respectively.Finally, the study concludes that there were significant effects of COIVD-19 on gold but not bitcoin prices. These results are consistent with gold’s traditional role as a safe-haven in crises, and bitcoin as a ‘virtual gold’ which has some similarities, and likely to be complementary rather than in a competion with gold.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".