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Record W3149871119 · doi:10.1111/caje.12505

The life cycle of trading activity and liquidity of Government of Canada bonds: Evidence from cash, repo and securities lending markets

2021· article· en· W3149871119 on OpenAlexaffvenueabout
Narayan Bulusu, Sermin Gungor

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsBank of Canada
Fundersnot available
KeywordsStylized factMarket liquidityBondBond marketCashMonetary economicsBusinessFinancial marketEconomicsFinancial economicsFinance

Abstract

fetched live from OpenAlex

Abstract This paper documents stylized facts about the life cycle of trading activity and trading cost (i.e., market liquidity) of Government of Canada (GoC) bonds. Using a unique data set from the cash, repo and securities lending markets, we document three stylized facts. First, the trading activity of all GoC bonds follows an inverse U‐shape across all three markets, with activity peaking during the benchmark (on‐the‐run) phase. Second, the level of activity exhibits considerable heterogeneity across bonds with different maturities: shorter‐term bonds are more actively traded in the cash and repo markets, while longer‐term bonds are more active in the securities lending market. Finally, in contrast to the conventional notion that a bond becomes less liquid as it ages, our trade‐based measures indicate that the transaction cost of a GoC bond remains low in the later stages of its life cycle.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.095
GPT teacher head0.171
Teacher spread0.076 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2021
Admission routes3
Has abstractyes

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