Interbank Asset-Liability Networks with Fire Sale Management
Bibliographic record
Abstract
"Raising liquidity when funding is stressed creates pressure on the financial market. Liquidating large quantities of assets depresses their prices and may amplify funding shocks. How do banks weathering a funding crisis contribute to contagion risk? We propose a model to study the transmission of distress in a banking system that has been hit by a funding shock. The model captures (1) the indirect effects of price impacts due to the optimal reinvestment of banks’ asset mixture and (2) the direct network effects if banks cannot fully pay back their interbank exposures. We demonstrate how banks liquidate assets in equilibrium and at what price, and we examine the role of regulators in stabilizing prices in a funding crisis. Our empirical results, based on the Canadian supervisory data, show that even very large funding shocks are unlikely to trigger a cascade of defaults. However, losses incurred on liquidated assets in fire sales may be substantial. Nevertheless, banks are less likely to overreact or sell more than strictly necessary when they take other banks' actions into account. "
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".