Bibliographic record
Abstract
A swap agreement is a financial arrangement wherein two counterparties agree to exchange cash flows over a period on a pre-arranged basis. In an interest rate swap the exchange is between interest cash flows based on a fixed rate and those that are determined based on a variable rate. Thus one party will agree to pay a fixed interest rate on a notional principal for a certain period in exchange for receiving interest cash flows based on a variable interest rate set periodically. The variable interest rate is determined with reference to an agreed upon index. Typically the variable rates will be a certain percentage above the interbank lending rates such as LIBOR. In the international context interest rate swaps are often a combination of interest rate and currency swaps. In a currency and interest rate swap fixed interest cash flows on a nominal principal denominated in one currency will be exchanged for floating rate interest cash flows in another currency. In this paper our focus is on interest rate swaps in a domestic context only. Interest rate swaps are increasingly being used as a risk management tool. If a firm borrows on a variable interest rate it is exposed to the risk of changing interest rates in the future. To mitigate this risk the firm may enter into a swap contract wherein it will pay fixed interest on a notional principal to the swap dealer and, in turn, receive variable interest cash flows from the dealer. This effectively protects the firm from changing interest rates. When the variable interest declines the firm’s cash outflow of interest on the borrowing will be less and so will be the receipts from the swap dealer. When the variable interest goes up the increased borrowing cost will be offset by the increased receipts from the swap dealer. Though it is possible to manage the interest rate risk through other exchange traded derivatives like interest futures and options, an interest rate swap has the advantage of customization. The disadvantage is that, unlike exchange traded futures or options, terminating a swap may not be a simple process and can be costly. Although long common in the corporate sector, the use of interest rate swaps among non-financial public institutions, including universities, has increased in the past decade. Given the nature of cash flows and short term assets that are typically carried by universities it is not clear whether interest rate swaps are true hedges or un-hedge an existing natural hedge and create risk. Recently, for example, Harvard University lost US $345.3 million in terminating its interest-rate swaps. It is the purpose of this paper to study the use of interest-rate swaps in a sample of Canadian universities and investigate whether they are true hedges or actually increase a university’s financial risk. An attempt will be made using management control, organizational design concepts, and accounting theory to explain the prevalence of interest rate swaps among Canadian universities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".