Dependencies and Volatility Spillovers among Chinese Stock and Crude Oil Future Markets: Evidence from Time-Varying Copula and BEKK-GARCH Models
Bibliographic record
Abstract
This paper investigates co-movements among the Chinese stock market, Shanghai International Energy Exchange (INE) crude oil futures and West Texas Intermediate (WTI) crude oil futures. We use Copula models to capture tail dependencies and employ the VAR-BEKK-GARCH model to examine the direction of volatility spillovers. We find that there are positively time-varying dependency relationships among the three markets. Compared with the corresponding upper-tail dependencies, the lower-tail dependencies were larger before the COVID-19 pandemic while relatively weaker after the breakout of the pandemic. Before the COVID-19 pandemic, there was only a statistically significant volatility spillover from WTI crude oil future market to the INE crude oil future market. After the breakout of the COVID-19 pandemic, there were statistically significant volatility spillovers in the two pairs of markets, namely, the WTI–INE and Chinese stock–WTI. However, we only find statistically significant evidence of unidirectional volatility spillover from the Chinese crude oil future market to the Chinese stock market during the pandemic.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".