Crude Oil Price Shocks and Stock Market Volatility: Evidence From China
Bibliographic record
Abstract
As the first international futures variety in China, crude oil futures, its price influence and function play has attracted much attention at home and abroad, from the perspective of market performance, crude oil futures have had a greater impact on the capital market since its launch, and what needs to be further studied is the quantitative degree and complexity of the impact of crude oil futures price fluctuations on stock market fluctuations. The daily data from March 26, 2018 to July 5, 2022 were selected to study the influence of domestic crude oil futures prices on domestic Shanghai and Shenzhen stock index by Granger causality, cointegration test and smooth transition regression model. The study shows that the price and yield of domestic crude oil futures have a one-way guiding effect on the domestic Shanghai and Shenzhen stock indexes and yields, but their guiding effect on the Shenzhen component index is greater than that of the Shanghai Composite Index. Domestic crude oil futures prices and Shanghai and Shenzhen stock indexes have a long-term similar negative cointegration relationship. The positive and negative impact of the domestic crude oil futures price yield on Shanghai and Shenzhen stock index yields is non-linear and asymmetrical, but the mechanism of impact on the two stock markets is different, for the Shanghai stock market, the negative impact of the crude oil futures price yield is greater than the positive shock impact, for the Shenzhen stock market, the positive impact of the crude oil futures price yield is greater than the negative shock impact, and the impact on both stock markets was limited .Therefore, for domestic crude oil futures to become the global crude oil price benchmark, they also need to be continuously improved in terms of national policies, industry supervision, exchange rules and market system construction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".