When a Hedge Turns into Speculation: Interest Rate Swaps at Canadian Universities
Bibliographic record
Abstract
The present paper intends to develop an analytical condition for an interest rate swap (variable rate for fixed rate) to be beneficial and examines the incidence and effectiveness of swaps in the Canadian university sector. The paper demonstrates the lack of any cost advantage in a two-party swap through a contradiction analysis and then tests whether there is evidence that the use of this derivative is indeed acting (or not) as an effective risk management tool. The paper also applies a nonparametric Kruskal-Wallis test to determine whether the size of the university is a factor in the effectiveness of swap. Of the 31 Canadian universities using swaps, only five pass the analytical condition to be judged as an effective swap. The balance fails the test, indicating that the usage of the swap, in essence, unhedges a natural hedge that the institution had. The results also indicate that university size plays a role in whether the hedge is effective or not. This paper is unique in applying a quantitative test to determine swap effectiveness in the Canadian university sector. It also points to the necessity for management of these institutions to better understand the effects and uses of derivative financing instruments for hedging purposes.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".