Bibliographic record
Abstract
We investigate and forecast the term structure of sovereign spreads between major economies. The spreads are calculated as the difference between government bond Zero yields for Australia, UK, Japan, Canada and Switzerland against the US. We choose a dynamic latent factor model to capture the dynamics of the term structure and apply Principal Components Analysis (PCA) to extract the latent factors. As the term structure of sovereign spreads exhibits high correlation between different maturities it is a natural approach to compress the cross-section into a low-dimensional vector of factors which account for a large part the variation. Our results show that the variations in the term structure of sovereign spreads can be explained by a limited number of three factors. This 3-factor model fits the data well and is capable of replicating the variety of different shapes, forms and characteristics of the spread curves. The three latent factors can be interpreted as level, slope and curvature while the loadings on these 3 factors are similar in shape to the familiar Nelson-Siegel loadings. To test the out-of-sample performance of our 3-factor model, we carry out a forecasting exercise against the random walk and several natural competitor models. While our model’s forecasting capabilities for 1-months forecasting horizons are humbling, the model clearly outperforms its competitors when the forecast horizon lengthens. In particular, the 12-month ahead forecasts beat the competitors by a wide margin. These results hold across all analyzed maturities and sovereign spreads. We also test our models performance in different time periods and for different frequencies. Overall, our model performs surprisingly well under different and partly unique economic conditions.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".