Risk Analysis of the Stock Price Index of Countries Participating in the “Belt and Road” Initiative - Based on GARCH-VaR Model
Bibliographic record
Abstract
The “Belt and Road” Initiative has attracted worldwide attention since its initial stage. The initiative is to unite countries participating in the “Belt and Road” Initiative (B&R countries), to build a community with a shared future for mankind, and to achieve mutual benefit and win-win. Since the implementation of the initiative, China’s outward foreign direct investment (OFDI) has ushered in a new upsurge, and a large amount of money has been invested in B&R countries. However, China lacks experience in OFDI, as it has not been long since China engaged in OFDI. Besides, most of the B&R countries are developing countries with immature market. As the barometer of the macroeconomy, the stock market can reflect fluctuations of the real economy and forecast the development trend of the macroeconomy. To explore the opportunities and challenges brought by the “Belt and Road” Initiative to the stock market of B&R countries, this study selects 8 countries with the most active stock market among B&R countries, and analyzes the impact of the “Belt and Road” Initiative on the stock price index risk of the 8 countries. In this study, the data are divided into 2 groups, i.e., pre-initiative and post-initiative. The GARCH-VaR model is used to calculate the stock price index risk of each country. The empirical results show that the “Belt and Road” Initiative has different effects on the stock price index risk of the 8 countries. After the “Belt and Road” Initiative, the fluctuation of China Shanghai Shenzhen 300 Stock Index Futures is far lower than that before the implementation of the initiative, and the stock price index risk of some countries has also been reduced.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".