Yield Curve Modelling: A Comparison of Principal Components Analysis and the Discrete-Time Vasicek Model.
Bibliographic record
Abstract
The term structure of interest rates is relevant to economists as it reflects the information available to the market about the time value of money in the future. Affine term structure models such as short rate models have been used in interest rate modelling over the past years to determine the mechanisms driving the term structure. Machine learning approaches are explored in this thesis and compared to the traditional econometric approach, specifically the Vasicek model. Multifactor Vasicek models are considered as the one factor model is found not adequate to characterize the term structure of interest rates. Since the short rates are not observable the Kalman filter approach is used in estimating the parameters of the Vasicek model. This thesis utilizes the Canadian zero-coupon bond price data in the implementation of both methods and it is observed from both methods that increasing the number of factors to three increases the ability to capture the curvature of the yield curve. The first factor is identified to be responsible for the level of the yield curve, the second factor the slope and third factor the curvature of the yield curve. This is consistent with results obtained from previous work on term structure models. The results from this work indicates that the machine learning technique, specifically the first three principal components of the Principal Component Analysis (PCA), outperforms the Vasicek model in fitting the yield curve.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".