Measures of Volatility, Crises, Sentiment and the Role of U.S. ‘Fear’ Index (VIX) on Herding in BRICS (2007–2021)
Bibliographic record
Abstract
We look into determinants (volatility, crises, sentiment and the U.S. ‘fear’ index) of herding using BRICS as our sample. Investors herd selectively to crises and herding is a short-lived phenomenon. Herding was highest during the global financial crisis (only China was affected). There was no herding during the European debt crisis and COVID. With regard to the relationship between volatility and CSAD (cross sectional absolute deviation)/herding, a lower CSAD (movement in a specific direction) brings about less volatility. However, a high volatility amplifies herding (reduces CSAD), especially in China. Russia and South Africa are unresponsive to volatility levels (low/high) and herding. We also observe volatility heterogeneity. Different volatility measures have different effects on different markets. There is limited evidence to suggest that sentiment (based on principal component) Granger causes herding/CSAD. Herding is a period and market variant and unrelated to crises. The U.S. ‘fear’ index has a short-lived, limited effect on CSAD/herding (during COVID only) for all countries except China. In addition, Granger causality analysis indicates a two-way relationship between the U.S. ‘fear’ index and CSAD/herding, unrelated to crises.
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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 | 0.003 |
| 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 | 0.001 | 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".