The Volatility Spillover Effect Between the International Crude Oil Futures Price and China’s Stock Market - Multivariate BEKK-GARCH Model Based on Wavelet Multiresolution
Bibliographic record
Abstract
This study aims to analyze the volatility spillover effect between the international crude oil futures market and China’s stock market. Using West Texas Intermediate (WTI) and the Shanghai Composite Index (SSEC) to represent the international crude oil futures market and China’s stock market respectively, this study selects data of WTI and the SSEC from August 10, 2007 to August 10, 2017. It processes these data via wavelet multiresolution to decompose them into different levels and then builds the data model based on the BEKK-GARCH model. By testing the parameters through the Wald test, it further explores whether the volatility spillover effect exists between WTI and the SSEC. Empirical evidence finds that the volatility spillover effect between WTI and the SSEC is significant in the short run, while, however, such a volatility spillover effect does not exist in the medium and long term.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".