Deposit Insurance and Forbearance Under Moral Hazard
Bibliographic record
Abstract
Abstract We study the efficacy of forbearance using a real options approach. Our model endogenizes moral hazard embedded in credit risk undertaken by the bank. The bank's interest rate risk is modeled as duration mismatch. Other modeling improvements over previous studies include such features as stochastic interest rates and deposits, continuous interest payments on an ongoing deposit portfolio, and a stochastic forbearance period. We find that the bank does have an incentive to engage in undue risk taking. Even in the presence of moral hazard, however, forbearance can still be a desirable course of action in reducing the FDIC's expected liability. In addition, the capital ratio plays an extremely important role in determining the fair insurance premium. Finally, using the mismatch of asset and deposit durations as the correct measurement of interest rate risk, our model reveals that an optimal asset variance may exist for a particular bank, contrary to what the contingent claims framework would predict. Therefore, we resolve the puzzle that banks in practice do not increase asset risk to take full advantage of the limited liability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".