Correlations and Volatility Spillovers Between WTI, Natural Gas, and Stock Markets During COVID-19 and the Russo-Ukrainian War
Bibliographic record
Abstract
This study investigates the extent of time-varying volatility and correlations between crude WTI (West Texas Intermediate), Natural Gas, and stock markets in the significant WTI-exporting (Russia, Norway, Canada) and WTI-importing (USA, China, Japan) countries during the COVID-19 crisis and the Russo-Ukrainian war. We employ the BEKK- MGARCH methodology with daily data of Brent prices Gas prices and six stock markets covering the period from 01 January 2020 to 30 October 2022. We find evidence of bi-directional transmission and volatility linkages between Gas and WTI and between WTI and all assets studied except Russia. While we document a negative relationship between the past conditional volatility of WTI and the current level of volatility of Russia, the past volatility of Russia positively affects the current volatility of WTI. Finally, the time-varying conditional correlations exist between crude WTI, Natural Gas, and stock markets during COVID-19 and the Russo-Ukrainian war.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".