What Matters in Determining Capital Surcharge for Systemically Important Financial Institutions?
Bibliographic record
Abstract
One way of internalising the externalities each individual bank imposes on the rest of the financial system is to impose capital surcharges (KS) on them in line with their systemic importance. Given the complexity of the financial system and the resulting difficulties in measuring systemic importance, it is sometimes argued to simply apply higher KS to larger banks, abstracting from other factors like interconnectedness. In this chapter, the authors consider different network structures of the banking system that are characterized by two different centrality measures. Their main finding is that size alone is not always a good proxy for systemic importance and must be supplemented with detailed information on interbank exposures. A relatively small bank playing an outsized role in the interbank market might be more systemic, and thus garner a higher capital surcharge, than a less connected bank of somewhat larger size. Alternatively, if the centrality of banks in an interbank network is positively correlated with their size, then proxies of a bank’s systemic importance largely based on size are sufficient indicators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".