Volatility discovery in G-7 stock markets based on evidence from realized kernels
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates empirically the volatility discovery hypothesis in G-7 financial markets by applying a Fractionally Cointegrated Vector Autoregressive (FC-VAR) model directly to realized kernels instead of returns. We further enhance our analysis using Wavelet Coherence to verify long and short-term co-movements, in both time and frequency domains. Our main findings reveal that the Canadian and UK markets have an equilibrium in volatility discovery with the US market. Conversely, empirical evidence support that the markets of Germany, France, Italy, and Japan lead the volatility discovery compared to US, suggesting potential diversification opportunities. Regarding the volatility discovery hypothesis, our analysis indicates that the most non-US markets (except for Canada and the UK) often lead in volatility response, likely due to exogenous shocks located mainly outside US (i.e., pandemia, Russo-Ukrainian war) during the sample period. This study contributes to the existing providing a broader understanding of financial market dynamics.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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