Emergency Liquidity Facilities, Signalling and Funding Costs
Bibliographic record
Abstract
In the months preceding the failure of Lehman Brothers in September 2008, banks were willing to pay a premium over the Federal Reserve’s discount window (DW) rate to participate in the much less flexible Term Auction Facility (TAF). We empirically test the predictions of a new signalling model that offers a rationale for offering two different liquidity facilities. In our model, illiquid yet solvent banks need to pay a high cost to access the TAF as a way to signal their quality, in exchange for more favourable funding in the future. Less solvent banks access the less costly and more flexible DW in case they experience an unexpected run, paying a higher future funding cost. The existence of two facilities with different characteristics allowed banks to signal their level of solvency, which helped to decrease asymmetric information during the crisis. Using recently disclosed data on access to these facilities, we provide evidence consistent with these results. Banks that accessed TAF in 2008 paid approximately 31 basis points less in the interbank lending market in 2010 and were perceived as less risky than banks that accessed the DW. Our results can contribute to a better design of liquidity facilities during a financial crisis.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".