The Structural Difference of Shanghai Stock Index before and after 2008: A Copula Based Analysis
Bibliographic record
Abstract
The year 2008 witnessed the greatest joint stock reform and financial crisis in Chinese history. After these two cases, significant changes have taken place in investors’ behaviors worldwide, along with which is the occurrence of structure change in stock market. In this paper, we employ copula model to simulate the joint distribution between Shanghai Stock Index (SSE) and Chinese Shanghai Index 300 (CSI 300), to find out structure change in Chinese stock market before and after 2008. From results of empirical studies, we get conclusions that the main nature of Chinese stocks market is symmetric, in both marginal and joint distributions. Via the changes of Copula types, upper and lower tail coefficients and Kendall coefficients, we can measure the structure change in Chinese stock market, and get further conclusion about investors’ behaviors change. Before 2008, there is an equal power in quitting market and longing, while diversified investors adjusted their expectation uniformly after this year. Testing results show that the general dependence structure of CSI 300 and SSE is highly dependent and symmetric in most cases. From the distribution of upper and lower tail coefficients, we can draw the conclusion that stratified investors are mainly focused on two tasks, after this year, to close the position on stocks with high correlated stocks market and to maintain market value of stocks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".