Quantile-Time-Frequency Connectedness in Global Equity Markets: Evidence from BRICS and G7 Economies
Bibliographic record
Abstract
We examine the quantile-time-frequency connectedness of stock returns among BRICS and G7 markets over the period January 2000 to January 2024, employing the Quantile Vector Autoregression (QVAR) model. Our findings reveal that spillover effects intensify during periods of extreme market conditions, compared to more tranquil phases. Furthermore, the stock markets of France, Germany, the United States, the United Kingdom, Italy, and Canada emerge as primary sources of contagion, whereas the BRICS markets and Japan primarily act as recipients across all quantile regimes. The frequency-quantile decomposition reveals that short-term dynamics primarily drive the net transmission of shocks at both the median and upper quantiles, whereas long-term dynamics are dominant at the lower quantile, indicating more persistent effects during market downturns. Finally, we construct investment portfolios based on the Minimum Connectedness Portfolio (MCP) approach and evaluate them through average portfolio weights and Hedging Effectiveness (HE) ratios. The results demonstrate that G7-based portfolios tend to have lower average weights and higher hedging efficiency, implying greater diversification benefits and enhanced risk mitigation performance compared to BRICS-based portfolios.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".