RETURN AND VOLATILITY SPILLOVER ACROSS STOCK MARKETS OF THE US AND ITS MAJOR TRADING PARTNERS
Bibliographic record
Abstract
To Analyze The Effects Of Return And Volatility Spillovers Between The Us Market And Its Major Trading Partners Using Weekly Stock Market Returns From January 2011 To December 2019. The Findings Provide Credence To The Hypothesis That The Us Market Has Been The Most Influential In Terms Of Return Spillover (259.5) To Other Stock Indexes, With The Biggest Return Spillovers Occurring In The Canadian Market (58.4) And The German Market (57.0). In A Similar Manner, The Us Market Has Been The Biggest Transmitter Of Volatility Spillover (202.3) To Other Stock Indexes, With The Highest Volatility Spillovers Occurring In The Canadian Market (51.8) And The German Market (45.8). The Us Market Has Been The Most Influential Based On Return And Volatility Spillovers. This Finding Also Suggests That The Us And Chinese Markets Are The Least Vulnerable To Foreign Shocks. In Contrast, The Canadian Market Is The Most Vulnerable To External In Terms Of Return Spillover And Volatility Spillover. As A Consequence, Chinese Stock Market Participants Can Still Reap The Rewards Of Diversity. During The Sample Period, However, Integration Across Developed StockMarkets Has Increased, Reducing The Benefits Of Diversity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".