Market Discipline of Canadian Banks’ Letters of Credit Activities: An Empirical Examination
Bibliographic record
Abstract
While the motivation and riskiness of US off-balance sheet banking activities have been studied both theoretically and empirically, no such study has been found dealing with Canadian off-balance sheet banking activities, although such activities are numerically huge, and growing larger each year. This article provides support for a market discipline hypothesis of Canadian bank letters of credit activities by employing several market measures of risk from one-factor and multi-factor models, and an implied asset volatility from the option-pricing model. Furthermore, it examines both price and quantity response of off-balance sheet activities in the Canadian banking market by employing a tobit analysis to assess the robustness of our conclusions about market discipline. The results indicate that various market measures of risk and letters of credit are negatively related. Moreover, banks with greater portfolio risk measured in terms of equity and asset risk, high leverage and interest rate risk are less likely to issue letters of credit.
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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 | 0.006 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".