Re-examining covariance risk dynamics in international stock markets using quantile regression analysis
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Bibliographic record
Abstract
This investigation is one of the first to adopt quantile regression (QR) technique to examine covariance risk dynamics in international stock markets. Feasibility of the proposed model is demonstrated in G7 stock markets. Additionally, two conventional random-coefficient frameworks, including time-varying betas derived from GARCH models and state-varying betas implied by Markov-switching models, are employed and subjected to comparative analysis. The empirical findings of this work are consistent with the following notions. First, the beta smile (beta skew) curve for the Italian, U.S. and U.K. (Canadian, French and German) markets. That is, covariance risk among global stock markets in extremely bull and/or bear market states is significantly higher than in stable periods. Additionally, the Japanese market provides a special case, and its beta estimate at extremely bust state is significantly lower, not higher than that at the middle region. Second, the quantile-varying betas are identified as possessing two key advantages. Specifically, the comparison of the system with quantile-varying betas against that with time-varying betas implied by GARCH models provides meaningful implications for correlation-volatility relationship among international stock markets. Furthermore, the quantile-varying beta design in this study relaxes a simple dual beta setting implied by Markov-switching models of Ramchand — Susmel (1998) and can identify dynamics of asymmetry in betas.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it