Estimations of Term Structure of Interest Rate Model Based on EKF and UKF Approaches
Bibliographic record
Abstract
In the CKLS general framework,this paper introduces approaches of estimation for equilibrium models of term structure of interest rates based on the extended Kalman filter(hereafter EKF) and unscented Kalman filter(hereafter UKF).Using fourteen years of daily Canadian zero-coupon bond price data,we make a survey and contrast between the estimation performances of the EKF-based and UKF-based algorithms.The empirical result comes to a conclusion that the UKFbased algorithm introduced in this paper offers a superior performance to the EKF-based one,which is used as a standard method to yield the likelihood function in literature.The superiority is further magnified when it comes to a strong nonlinear system with a non-Gaussian distribution.Furthermore,the fitness of Vasicek model and CIR model is compared with the data above using the UKF-based algorithm,which indicates that both the models perform well in capturing the dynamics of the interest rates,while Vasicek model does even better.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".