Exchange rates and oil price under uncertainty and regime switching: A Markov-switching VAR approach
Bibliographic record
Abstract
Purpose - This paper analyses the effects of the US economic policy uncertainty index and oil price changes on the dollar exchange rate over a monthly period from January 2006 to August 2020. Methods - This paper uses the Markov-switching Vector Auto-Regressive (VAR) model. Findings - The results show that the sharp decline regime in the exchange rate is the most stable. In addition, the impact of the oil price on the exchange rate of the concerned currencies is stronger than the effect of EPU on the exchange rate of these currencies. We also find that most of the effects of oil prices were negative, while positive for the Canadian dollar and the Japanese yen exchange rate. Implications - Addressing this investigation contributes to many of the areas covered in recent macroeconomic and finance research. Moreover, such research can help predict changes in currency and oil prices better and create profitable investment and hedging strategies for currencies and oil. Originality - We consider the effect of economic policy uncertainty (EPU) and oil price changes on the relationships between those markets and study these relationships under different market conditions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".