Banks' Capital, Securitization and Credit Risk: An Empirical Evidence for Canada." HEC Working Paper No
Bibliographic record
Abstract
Financial supports by the Initiative on the New Economy (INE) program of CRSH (Canada) and FCAR (Quebec) are acknowledged as well as comments by Michel Crouhy on a first version of the paper. Résumé: La croissance rapide des activités hors bilan soulève un nombre intéressant de questions au sujet de la relation entre le capital des banques, la titrisation et le risque. Cet article est le premier qui étudie cette relation empiriquement. Les résultats pour le Canada durant la période 1988-1998 montrent que: a) la titrisation a des effets négatifs sur les ratios de capital, et b) il existe un lien statistique positif entre la titrisation et le risque des banques. Ces résultats semblent confirmer la prédiction de Kim et Santomero (1988) à l’effet que les banques peuvent être induites à augmenter leur risque sous les règles actuelles de capital pour le risque de crédit. Abstract: This paper is the first attempt that empirically investigates the relationship between banks capital, securitization and risk in the context of the rapid growth of off-balance-sheet activities in the Canadian financial sector. The evidence over the 1988-1998 period indicates that a) securitization has negative effects on both Tier 1 and Total risk-based capital ratios, and b) there exists a positive statistical link between securitization and banks ’ risk. These results seem to accord with Kim and Santomero (1988) who concluded that banks might be induced to shift to more risky assets under the current capital requirements for credit risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".