Yield Spreads on Government Benchmark Bonds: Cross Country Evidence
Bibliographic record
Abstract
Government Benchmark Bond?s yield differentials between countries may provide evidence movements in changes risk factors and its expectations. In most countries, the risk perception on long term bond?s interest rate has changed in decrease year by year. Comparing specification yield and cointegration of 10-year Government Benchmark Bond between countries makes it possible to understand whether there is any changes perception of risk. The perception of risk may appoint banking and corporate risk premiums in their bond market. Besides, an integrated government bond market has an importance for monetary mechanism to the country. And it is related to financial sector activity like hedging and pricing debt, and it is supports international factors affect spreads because they change the perceived default risk of government bonds in the countries. Because of cointegration between markets is highly important for changing effects of risk expectation which is relatively different from country to country. The aim of this paper is to learn 10-year Government Benchmark Bond?s Behavior and effecting to the other county?s benchmark bond. For this purpose, we examined Abnormal Return and Cumulative Abnormal Return of Australia, Canada, Euro Zone, UK, Japan and US?s 10-year Government Benchmark Bond monthly rate from January 2000 to April 2015 period. It is analyzed 184 nominal repurchase rates in monthly base for each countries benchmark bond as a time series. In calculating Abnormal Return, US?s Government Benchmark Bond?s Rate has determined as a comparison parameter to each countries series. According to cumulative abnormal returns, we have detected which country has dramatically dropped against US?s benchmark bond yield. After this evidence, we have taken into account any cointegrating relationship among the countries? benchmark bonds. We analyzed Johansen (1988) Cointegration Test to determine long term relationship between them. In addition to Johansen Cointegration test, we need to determine short term effect for each series. In this study, we tested Vector Error Correction Model (VECM) to calculate coefficient to hold balance between cointegration. We also tested Granger Causality (2004) to determine which benchmark bond has causality behavior to the other government benchmark bond.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".