The infernal couple China-Oil Price and the Responses of G7 Equities: A QQ Approach
Bibliographic record
Abstract
China’s growingly sluggish economy and collapsing oil prices sent ripples through global equities, and G7 countries (United States, United Kingdom, Germany, Canada, Japan, France, Italy) are no exception in this regard. In this article, we address how react G7 stock markets to oil price under potent uncertainty encompassing the extent of China’s slowdown. The main feature of this study is its use of an unwonted method, dubbed the quantile-on-quantile (QQ) approach. Even though this method is based on the quantile \nregression paradigm, it departs from the conventional framework as the exogenous variable may be itself a quantile. This technique is devoted to unsettled context where standard methods are malapropos. Captivating findings have been shown. First, the QQ approach views the G7 stock markets responses to oil price as highly heterogeneous among taildistributions, where consistent with the notion of asymmetry. Second, the equities of Germany, Italy, Canada and United Kingdom (in this order) are typically more responsive than France, Japan and United States towards oil price. Third, even if the fears over China’s \nworsening outlook sends G7 countries into a deeper slowdown, United Kingdom, Japan, France and United States appear better positioned to weather the storm. The oil-dependence profile, the dominance of companies belonging to cyclical industries in the stock market index, the role of monetary policy in containing speculative bubbles, and the forceful quantitative easing have been offered to spell out the convolution of the focal issue.
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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 | 0.004 | 0.012 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".