Heterogeneous Responses to China and Oil Shocks: the G7 Stock Markets
Bibliographic record
Abstract
Given its size and integration with the global economy, Chinese economic downturn could have momentous spillovers to the rest of the world and result in a decline in oil prices. This article investigates whether the Chinese economic slowdown and the oil prices affect the G7 stock market. We use a Quantile-on-Quantile regression approach to capture the correlation structure between the G7 stock returns and oil price returns under different G7 market conditions with considering nuances of oil price movements and Chinese slowdown. Data are employed over the period of January 1999 ~ December 2015. Our results show that the responses of G7 stock returns to China and oil shocks are likely to be asymmetric, nonlinear and country-specific. The stock market returns of Germany, Italy and Canada appear the most vulnerable to these shocks. Our results suggest that international investors consider the states of stock market returns and oil price alongside with the interaction effect between China's economic slowdown and oil market.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".