Dynamic Changes in Noble Metal Prices under Long-term Uncertain Situation: Evidence from Normalized Covid-19 Pandemic
Bibliographic record
Abstract
Covid-19 has severely hit global financial market since its outbreak. Investors who withdrawal funds in other areas will seek protection under the precious metals, a market known for its safety. This paper assesses the global and China regional daily new confirmed cases’ impacts on gold and silver price. A VAR and an ARMA-GARCH model are built to analyze the changes of the value and volatility. The paper finds that Chinese epidemic still has a positive impact on international precious metal market price while the effects other continents are little. As for volatility, the shock brought by the virus has no significant influence on gold and silver volatility. The paper aims to study how precious metal investors response to newly confirmed cases under normalized Covid-19 Pandemic. Based on the results, global gold and silver market overreacts to the cases in China in comparison with other lands; Thus, the research suggests Chinese Government to further stabilize local pandemics while investors decrease the reaction towards China side’s epidemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".