Spillovers of the COVID-19 Pandemic: Impact on Global Economic Activity, the Stock Market, and the Energy Sector
Bibliographic record
Abstract
In this study, we examine the effect of the COVID-19 pandemic on global economic activity, the stock market, and the energy sector considering the sizable damaging impacts in these crucial aspects. Our results, based on the structural vector autoregression (SVAR) model for the data from 21 January 2020, to 26 February 2021, indicate that the COVID-19 cases significantly and negatively impact all the endogenous variables such as Baltic dry index (BDI), MSCI world index (MSCI), and MSCI world energy index (MSCIE). Our results also reveal that of the three variables, the stock markets indices (MSCI and MSCIE) are comparatively more affected by COVID-19 cases. The findings imply that the stock markets are more sensitive to the COVID-19 pandemic than the real economy. The results further indicate that of the three variables, the MSCIE index is the most affected by COVID-19 due to two factors: one is the dwindling power consumption caused by COVID-19 and the other is the decline in oil price because of the Russia–OPEC price war. Our findings enhance the understanding of the spillover impacts of the global health crisis on economic activity, the stock market, and the energy sector. Moreover, our study offers insights for policymakers and governments into the relationship dynamics of COVID-19 that would help them be more cautious in taking preventive measures against the health crisis to save the economy, the stock market, and the energy sector from falling into a more deepened crisis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".