Spillover Effects among the Main Stock Markets in China’s Capital Market Opening Process
Bibliographic record
Abstract
With the opening and development of China’s capital market, mainland China and the world’s stock market is increasingly close. This paper explores the volatility spillover effect between China’s stock market and the world’s major ones to study the transmission path of stock market risk and provides policy suggestions for promoting development of China’s stock market. We selected daily returns of four large indices from December 26, 1996 to March 1, 2016 as the main object to study and analyze Shanghai Composite Index, Hong Kong Hang Seng Index, American Dow Jones Index, and British Financial Times Index. According to the different degree of openness of China’s capital market, the samples are divided into five sub-stages. Granger test and DCC-MGARCH model are used to analyze the causal relationship and dynamic correlation of the mainland stock market and the major ones in the world. To avoid the volatility and risks, we found corresponding measures. The empirical results show that the implementation of WTO and the QFII system cannot effectively promote the internationalization of the stock market in China. However, the exchange rate reform of RMB and Shanghai-Hong Kong Stock Connect Program have greatly improved the linkage between Chinese and international stock markets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".