The Canadian Dollar and the 2014 Oil Price Plunge: The Creation and Destruction of Realities
Bibliographic record
Abstract
The paper starts by relating general exchange rate determination theories.It then lays down exchange rate determination models pertaining to the Canadian dollar.The paper then transitions to the 2014 oil price plunge and attempts to show that expectations and speculation, rather than real economic factors, contributed the most to its happening.Its impacts on Canada, and more precisely on the USD/CAD exchange rate, are then presented.Finally, micro data (daily, hourly, minutely, and per second) is used to back the claim that the Canadian dollar has grown to respond to oil prices in a matter of a few seconds.The per second analysis suggests that the Canadian dollar adjusts to changes in oil prices within 3 seconds at most.Such a pattern can only be achieved by pre-programmed speculative computers.As compared to five major currencies, the superior responsiveness of the Canadian dollar to oil prices stems from an actionreaction sequence that happens over the course of 2 to 3 seconds.Contrary to neoclassical theory, speculation, both on oil futures and the Canadian currency, does have long term impacts on the Canadian dollar.In accordance with Lavoie (2014), this paper supports the claim that "speculative behaviour […], when excessive, can destabilize flexible exchange rates".
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".