Capturing Non-Linearity in the Term Structure of Interest Rates: A Fuzzy Logic Method of Estimating the Yield Curve
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper provides a new solution to the problem of estimating the yield curve from a sample of coupon bonds. Our approach builds on the work of McMulloch (1971, 1975) by using a new approach to formulate and estimate the discount function for US Government Issue Coupon Bonds. This new method uses fuzzy regression to estimate an arbitrary nonlinear function, rather than estimating a polynomial specification, after using fuzzy clustering to break the bond sample into various clusters based on term to maturity. Estimates from the fuzzy regression are used to calculate the discount function, yield curve and the theoretical prices for the in-sample securities. Finally, estimates of the standard errors of the yield curve, discount function, price estimates, and forward curves can be obtained by bootstrapping. Our fuzzy analysis provides a very flexible way of dealing with the inherent nonlinearities in the problem, without imposing an arbitrary functional form and it provides some encouraging results for the data that have been analyzed
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it