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Record W4402483517 · doi:10.1287/mnsc.2022.03133

LIBOR Discontinuation and the Cost of Bank Loans

2024· article· en· W4402483517 on OpenAlexaff
Jeong‐Bon Kim, Chong Wang, Feng Wu

Bibliographic record

VenueManagement Science · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLiborDiscontinuationEconomicsFinancial systemBusinessMonetary economicsActuarial scienceInterest rateInternal medicineMedicine

Abstract

fetched live from OpenAlex

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 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.231
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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