Have Stock Markets Become Less Volatile After the Great Recession?
Bibliographic record
Abstract
This paper investigates volatility modeling in light of the 2008 global financial crisis. The study was motivated by the measures and regulations introduced by most of the countries following the shock to stabilize their financial markets. The theoretical proposition is that these measures should succeed in reducing volatility which would be modeled differently following the crisis. The adopted ARMA-GARCH process included positive and negative trading volume change to capture the asymmetric effect of trading volume on market volatility for seven international markets. The results indicate that the majority of these markets were not so successful in reducing volatility following the crisis. There is evidence of volatility persistence which dissipates very quickly. Although volatility is modeled differently before and after the crisis, each market is modeled uniquely. The effect of trading volume was found to be asymmetric. Only positive change was a valid predictor. Detailed discussions of the results, implications, and recommendations are provided.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".