Crude signals: Asymmetric adjustments in energy markets to global oil price shocks
Bibliographic record
Abstract
The paper empirically investigates the asymmetries pass-through rate of international oil prices to energy markets in eight key developed economies. To examine this, we apply an asymmetric ARDL model to split the effects of positive and negative partial sum decomposition in WTI oil price on energy markets in the short- and long-run. Moreover, we use cumulative dynamic multipliers to contribute to the understanding of the time path and adjustment process of energy stock responses to oil price shocks. Our results show that Germany, Italy, and the US exhibit asymmetry, with oil price variations affect differently to energy markets in these countries. Alternatively, energy markets in Australia, Canada, France, Japan, and the UK exhibit long-run symmetry, indicating that whether oil prices rise or fall, the pass-through to energy prices follows a proportional pattern in the long run. However, dynamic multiplier captures short-term asymmetries in several cases (Australia, Canada, France and Japan and the UK) and highlights different speeds of convergence with some markets adjusting rapidly while others requiring longer periods to get back to the equilibrium position. In particular, our results indicate that oil price increases have a larger pass-through effect than decreases on a few energy markets, while for others, the effect remains symmetric.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".