Dynamic and asymmetric connectedness among fossil energies and stock markets of the Belt and Road countries under shocks from extreme events
Bibliographic record
Abstract
This study investigates the dynamic and asymmetric return connectedness between fossil energies of crude oil and natural gas, and stock markets of the Belt and Road countries (the B&R stock markets) under shocks from extreme events (e.g. the COVID-19 pandemic and the Russo-Ukrainian war) from 2019 to 2023 by using the time-varying parameter vector autoregression model (TVP-VAR) with the asymmetric connectedness indicator and multilayer spillover networks. We find that: (i) risk spillover between fossil energies and the B&R stock markets is more sensitive to negative information on price changes than positive information, and the asymmetry of the connectedness is much larger during the periods with exogenous shocks induced by extreme events of the COVID-19 pandemic and the Russo-Ukrainian war. (ii) the level of risk spillover between fossil energies and the B&R stock markets has significantly increased after the outbreak of extreme events, and the global shock from the COVID-19 pandemic has more widespread and greater impact on the risk spillover than the geopolitical shock from the Russo-Ukrainian war. (iii) fossil energies perform as risk receivers throughout the full sample period, and risks are primarily transferred from high-income countries to low-income countries within the B&R stock markets, this phenomenon is also intensified by the extreme shocks. Our findings provide valuable guidance and have economic implications for both investors and policymakers worldwide to diversify and manage the risks within the global fossil energy market and the B&R stock markets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".