DOES GOLD RETAIN ITS HEDGE AND SAFE HAVEN ROLE FOR ENERGY SECTOR INDICES DURING COVID-19 PANDEMIC? A CROSSQUANTILOGRAM APPROACH
Bibliographic record
Abstract
The Outbreak of the COVID-19 Pandemic has caused unprecedented risk and uncertainty in the global financial markets. The shattered investor's faith in the Global Financial system has stimulated the need to explore safe haven assets to mitigate risk and safeguard wealth during such turmoil. Therefore, this paper addresses the widely mooted hedge and safe haven property of gold against extreme downturns in the stock market energy sector indices during COVID-19 distress. The sample countries considered comprises of the USA, Saudi Arabia, UAE, Russia, Canada, India and China being strategically linked to gold and oil commodities. Splitting the sample period into Pre-COVID period from 30th June 2019 to 30th December 2019 and During-COVID period from 31st December 2019 to 30th June 2020 the study employs bivariate cross-quantilogram of (Han, et al., 2016) to examine directional predictability in quantiles between gold and energy sector indices. The results confirm the inability of gold to showcase its pronounced hedge and safe haven role before the COVID-19 crises. Specifically, Countries such as Saudi Arabia, Russia and Canada show a significant negative predictability from energy sector indices to gold thereby indicating its safe haven role during COVID-19 crises.Keywords: Gold, Safe Haven, COVID-19, Cross-quantilogram, Energy sector Indices.JEL Classifications: G01, G11, G15, Q40DOI: https://doi.org/10.32479/ijeep.10294
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".