MétaCan
Menu
Back to cohort
Record W7149610530 · doi:10.17132/2693-3179.1355

Canada: Bankers’ Acceptance Purchase Facility

2022· article· en· W7149610530 on OpenAlexaboutno aff
Corey N. Runkel

Bibliographic record

VenueJournal of Financial Crises · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsLiberian dollarInterest rateCommon value auctionAsset (computer security)Investment (military)PurchasingProduct (mathematics)

Abstract

fetched live from OpenAlex

Bankers’ acceptances (BAs) are a form of investment security guaranteed by banks to fund loans to businesses against their credit lines. In Canada, BAs underpin the Canadian Dollar Offered Rate (CDOR), the main benchmark used to calculate floating interest rates in Canada’s derivatives market. In 2018, BAs formed the largest segment of money market securities traded in the secondary market at around CAD 35 billion (USD 26 billion) per week. When asset managers and the country’s public pension providers began shedding BAs amid the COVID-19 pandemic in early 2020, CDOR spiked, and the effects threatened to ripple throughout the Canadian financial system. On March 13, 2020, the Bank of Canada (BoC) established the Bankers’ Acceptance Purchase Facility (BAPF). The BAPF conducted multi-rate reverse auctions with Canadian primary dealers for highly rated BAs of remaining maturities up to 76 days. In its first two operations, dealers sold the BoC the total offered amounts of CAD 15 billion and CAD 20 billion, and the BA market stabilized. The BoC bought another CAD 12 billion of BAs in four operations in April. It continued to offer to buy CAD 10 billion in weekly, then biweekly reverse auctions until October, with no further bids from banks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.229
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Financial CrisesSame topicBanking stability, regulation, efficiencyFrench-language works237,207