An Empirical Study on Effects of US Treasury Futures Market on the KTB Futures Market and Its Information Transfer Effect – Mainly after the Global Financial Crisis
Bibliographic record
Abstract
Since the Global Financial Crisis in 2008, funds have been moved to safe assets from previously preferred risky assets on a global basis. Moreover, the financial crisis ignited in the U.S.A. led to strong quantitative easing policies, which played a major variable in the monetary policies of the major countries. So, the US treasury yield rates and Korean counterpart have showed signs of being synchronized. On the other hand, foreigners’ investments on Korean bonds became accelerated; the amount invested to Korean treasury by foreigners as well as their influence in the Korean treasury market has been expanded. Particularly, investment on the 10 year treasury bonds has increased, which spread influence of the Korean treasury market. In this regard, the study analyzed effects of the US treasury market on Korean counterpart. In order to analyze the volatility transfer effects from US treasury market to the KTB future market, in consideration of the synchronized maturity dates of the treasury and the officially announced prices, data on US 10 year treasury futures index and Korean 10 year treasury futures index . GARCH model was used for empirical analysis. Effects of the daily volatility and direction of US 10 year treasury futures index on the Korean counterpart was analyzed. Through the analysis, it was confirmed that information was transferred to the yield of Korean 10 year treasury futures index from the US counterpart. The study will be able to help establish more rational and efficient strategy for bond investment and operation.
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 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".