The Extreme-Value Dependence Between the Chinese and Other International Stock Markets
Bibliographic record
Abstract
Extreme value theory (EVT) measures the behavior of extreme observations on a random variable. EVT in risk management, an approach to modeling and measuring risks under rare events, has taken on a prominent role in recent years. This paper contributes to the literature in two respects by analyzing an interesting international financial data set. First, we apply conditional EVT to examine the Value at Risk (VAR) and the Expected Shortfall (ES) for the Chinese and several representative international stock market indices: Hang Seng (Hong Kong), TSEC (Taiwan), Nikkei 225 (Japan), Kospi (Korea), BSE (India), STI (Singapore), S&P 500 (US), SPTSE (Canada), IPC (Mexico), CAC 40 (France), DAX 30 (Germany), FTSE100 (UK) index. We find that China has the highest VaR and ES for negative daily stock returns. Second, we examine the extreme dependence between these stock markets, and we find that the Chinese market is asymptotically independent of the other stock markets considered.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".