Currency risk management: simulating the Canadian dollar
Bibliographic record
Abstract
Purpose The purpose of this article is to investigate the return associated with a Canadian dollar (C$) investment in the USA under passive, random walk, value at risk, and Sharpe ratio strategies. Design/methodology/approach To comply with the purpose, this paper used a GARCH model, and used, as basic data, daily C$ exchange rates and weekly US and Canadian interest rates on 90‐day CDs, from January 2 to November 26, 2004. Findings The empirical results suggest that currency returns are positively correlated to risk; and that the return provided by the random walk strategy beats the other strategies considered in this paper. Practical implications The findings suggest that currency investment is similar to other forms of investment, since it shows a positive relationship between risk and return. It also supports the long‐standing belief that sophisticated strategies do not beat simple‐minded approaches such as a random walk strategy. Originality/value This paper uses a utility function to investigate the response of investors to risk and return under different aversion scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".