Volatility of main stock indexes: similarities and differences
Bibliographic record
Abstract
How correlated are the volatilities of stock markets indices all over the world? Is it possible to cluster the volatility indices? To examine the behavior of the volatilities of the main world stock market indices, we analyzed the daily data for SP500 (US), Shanghai Comp Index (China), FTSE100 (UK), CAC40 (France), DAX (Germany), SP/TSX (Canada), Bovespa (Brazil), Merval (Argentina), Nikkei 225 (Japan) during the period from January 4th, 2008 to April 11th, 2011. There are several possible methods to cluster the volatilities, we consider two of them. The First method consider the comparison of estimates of the parameters w in a APARCH model, which consider the baseline level of the volatility. The second method estimate the volatilities also using APARCH models and uses correlation coefficients to clusters these indices. It was possible to conclude that the crisis reached all considered stock indices in 2008. All the analyzed countries recuperated all the losses in April 2011, except China and Japan. Regarding the volatility it was possible to identify cluster of indices. A first group was composed by UK, France, Germany and US. Brazil and Canada present a similar pattern and they are in the same cluster of Argentina.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".