Bibliographic record
Abstract
With the London Interbank Offered Rate (LIBOR) being replaced by risk-free rate (RFR)-based alternative reference rates, the fundamental differences between the two benchmarking frameworks impose significant risks on banks. Exploiting the Financial Conduct Authority (FCA)’s announcement of the phase-out of LIBOR, we conduct a difference-in-differences analysis based on banks’ reliance on LIBOR and show that LIBOR discontinuation entails higher interest rate spread of bank loans. The result implies that banks tend to compensate for the LIBOR-to-RFR risks by passing on the transition costs to borrowers. This effect is attenuated if multiple benchmarks are already in use, for relationship lending, and among banks operating in a competitive environment. We further find that LIBOR discontinuation leads to more collateral and covenant requirements in loan terms. After the FCA announcement, banks are inclined to switch away from LIBOR dependence by referencing alternative rates. This paper was accepted by Victoria Ivashina, finance. Funding: J.-B. Kim acknowledges support from City University of Hong Kong; C. Wang acknowledges support from Hong Kong Polytechnic University and the National Natural Science Foundation of China [No. 71932003]; F. (H.) Wu acknowledges support from the General Research Fund [No. 13500820] from the University Grants Committee of Hong Kong. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03133 .
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".