CORRA: Explaining the rise in volumes and resulting upward pressure
Bibliographic record
Abstract
The Canadian Overnight Repo Rate (CORRA) measures the cost of overnight general collateral Canadian-dollar repurchase agreements (repos). Since late May 2024, the volume of trades that make up CORRA has increased and remained elevated. At the same time, CORRA started being consistently above the Bank of Canada’s policy interest rate. This upward pressure results entirely from industry-wide changes to the settlement period for cash bond trades on the secondary market, from two days to one. The change in the settlement period prompted a rise in volumes in the overnight repo market (which is CORRA-eligible) from the tomorrow-next repo market (which is not CORRA-eligible). In addition, this move has overwhelmingly been one way: demand from hedge funds to fund their long bond positions. This demand existed before but was always traded in the tomorrow-next market and thus activity in the tomorrow-next repo market has been decreasing by an amount comparable to the increase in the overnight market. We find this mechanical effect has accounted for up to 3 basis points of upward pressure on CORRA. We find no indications that any other factors are contributing to this pressure. Given the new dynamics since May, the Bank has amended the terms of its overnight repo operations. It has also subsequently conducted a series of operations to help reinforce the target for the overnight rate, which had deviated away from the Bank’s policy rate due to this mechanical adjustment. Overnight repos are routine operations that are part of the Bank’s operational framework for implementing monetary policy and reinforcing the policy interest rate.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".