Shadow banking and the dynamics of aggregate leverage: An application of the Kalman filter to cyclical leverage measures
Bibliographic record
Abstract
During the last decades, banks off-balance sheet (OBS) activities (e.g. securitization, trading and fee-based activities) have greatly contributed to the increase in bank risk. However, the standard financial indicators such as the Value-at-Risk and the accounting leverage, exclude these non-traditional activities, and neglect the increased risk market-oriented banking generates. In this paper, we study various measures of leverage in the context of shadow banking, relying on a dynamic setting, which features Kalman filter procedures and different detrending methods. Applying this framework to Canadian data, we can detect the increase in risk associated to banks new business lines years before what the conventional risk measures predict. We also find that the elasticity measures of leverage, compared to the simple balance sheet ratios like the ratio of assets to equity or the mandatory leverage measure, are generally more forward-looking indicators of bank risk, and better capture the cyclical pattern of bank leverage. The main contribution of this paper is to show that OBS activities exert a stronger influence on these leverage measures during expansion periods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".