A Comparative Analysis of the Nature of Stock Return Volatility in BRICS and G7 Markets
Bibliographic record
Abstract
Through globalization and financial market liberalization, the opening up of markets has increased cross-border investments as investors search for higher risk-adjusted returns. This ability to invest internationally has raised the attention given to emerging markets that offer higher risk-adjusted returns relative to developed markets. However, despite the growing importance of emerging markets, the literature on the nature of volatility in global markets is typified by generalizations of findings from developed markets. To fill this gap, this study comparatively examined the nature of stock return volatility in developed G7 and emerging BRICS markets. Broad market index data and GARCH models over the period 2003:01–2020:08 were employed. The study found evidence of volatility persistence, asymmetry, mean reversion and weak evidence of a risk premium in both emerging and developed markets. There was also evidence of significant differences in the nature of volatility within the two sets of markets. These volatility patterns in both groups cast doubt on the assertion that developed markets are more informationally efficient than emerging markets. Thus, markets in the same group may not always have the same nature of volatility, especially in the wake of structural events such as the COVID-19 global pandemic.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".