Assessing Systemic Risk Exposure from Banks and GSEs Under Alternative Approaches to Capital Regulation
Bibliographic record
Abstract
A key function of capital regulation is to mitigate the potential for systemic financial risk by maintaining public confidence in the ability of regulated market participants to honor their financial obligations in times of market stress. While it is well-known that the portfolios of banks and non-banks, especially those intermediaries specializing in mortgage securitization or in specialized mortgage lending, differ in important respects, debate over alternative capital regulations has yet to recognize the implications of these differences, despite the increasing importance of non-bank intermediaries in risk-sharing markets. This paper uses a simple two date discrete state space exchange economy with opportunities for moral hazard on the part of financial intermediaries to investigate the design of capital regulations to control systemic risk. Holding constant asset risks, we show that intermediaries that issue contingent liabilities may exhibit low or no risk of insolvency while holding significantly less capital than deposit-taking institutions because banks primarily issue claims that promise fixed payments in all states of nature. We also show that, rather than raising capital requirements, the control of systemic risk may involve lowering capital requirements and extending guarantees to liability-holders, without a necessary increase in expected subsidy payments, if such requirements account for shareholder incentives. Specifically, we analyze an example of regulatory policy in which lower capital requirements and an ex-post penalty schedule reduce systemic risk by increasing the volume of tradable securities exchanged and by offering a credible mechanism by which intermediaries can signal the true riskiness of their portfolios to liability-holders.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".