Term Structure of Interest Rates and Implied Market Frictions: The Min–Max Approach
Bibliographic record
Abstract
It is often assumed that financial markets are frictionless. Bond markets are illiquid and bond prices are observed with errors. The magnitude of these errors leads to violation of no–arbitrage conditions and, consequently, prevents researchers from obtaining an estimate of the term structure (TS) of interest rates. Researchers have had to settle for a second–best estimate of the TS (e.g., obtained via regression) at a cost of an economically unrealistic assumption of symmetric market frictions. The true shape of market frictions, however, is not known and generally is a highly complex issue. A no–arbitrage–based methodology that avoids making detrimental assumptions is developed here. It facilitates empirical investigation of the shape of the market frictions and of the TS that are simultaneously imputed from market data assuming “efficient” market frictions that minimize the maximum net arbitrage. The empirical investigation performed in the Canadian and U.S. markets shows that in both markets the frictions are asymmetric and the estimates of the TS produced via regression and our methodology significantly differ.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".