Government Bond Market Seasonality, Diversification and Cointegration: International Evidence
Bibliographic record
Abstract
Using international bond market data for the United States, Canada, United Kingdom, Germany, France, and Japan, several hypotheses are investigated. Market efficiency is addressed by testing for seasonality and cointegration. The seasonality results are mixed. When employing regression, a January effect is detected in several markets (United States, Germany, France, United Kingdom, and Canada) using local currencies. However, when using a nonparametric test, the January effect is supported only for France. Using U.S. dollar returns, regression results also detect a January effect for several markets (United States, Germany, France, and the United Kingdom). These results are not confirmed by a nonparametric test. Correlation analysis shows considerable diversification opportunities for short-term investors, with correlations rising over long horizons. Cointegration tests indicate that several of the markets share cointegrating vectors, increasing the possibilities of using other endogenous bond markets to better predict movements in a particular market.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".