Bibliographic record
Abstract
A revolving credit line is one of the most common forms of commercial bank loan. Fixing the interest rate and the maximum loan amount but not the utilization pattern introduces several types of uncertainty into the contract. In practice, in addition to the interest on the drawn amount, a variety of different fees and charges may be imposed, although generally not all at once. This leads to interesting optimal behavior for the borrower in the face of stochastic fluctuation in market interest rates and borrower credit quality. For example, the borrower can raise funds in the open market if the interest rate is lower there but has the option to draw against the line at the original rate if its creditworthiness weakens. Jones and Wu present a model incorporating these special features and explore how they affect optimal loan terms and borrower behavior. Interesting results include the fact that because of the borrower’s option to draw on the credit line when its creditworthiness weakens, the lender cannot make money on the deal without incorporating extra fees on top of the interest on the borrowed principal. TOPICS: Real assets/alternative investments/private equity , quantitative methods
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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 | 0.010 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".