Oil prices and exchange rates causality: New evidences from decomposed oil prices shocks and parametric in quantile analysis
Bibliographic record
Abstract
In this paper, the researchers reassess the causality between oil prices and exchange rates by applying the parametric quantile analysis to decomposed oil prices shocks and exchange rates returns data of both low income, emerging and developed oil exporting countries from 1993.11 to 2021.10. Unlike the existing researches using the causality in quantile analysis, our study outcomes support the causal relationship from exchange rates to oil prices shocks at upper and lower quantiles in developed oil exporting countries; this is also true regarding the bidirectional causality observed in low income and emerging oil exporting countries .These findings imply that, important positive and negative oil shocks cause extremes changes in the exchange rate returns of low income and emerging oil exporting countries and reciprocally. However only extreme fluctuations of exchange rate returns of developed oil exporting countries such as Norway and Canada can cause oil prices variations. The results of non-causality at middle quantiles also suggest that the monetary authorities in both developing and developed oil exporting countries resist the exchange rates adjustments when oil prices fluctuations are significant. From these results we recommend sound policies in order to mitigate internal and external shocks during crisis, structural reforms that support diversification of energy production by developing other energy sources and reduce crude oil dependence, as well as the whole economy diversification mostly for developing countries and finally, multiple exchange rates to diversify portfolio and hedge the risks associated to oil prices fluctuations for investors. Keywords: Exchange Rates, Oil Prices Shocks, Quantile Causality.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".