Does the US stock market information matter for European equity market volatility: a multivariate perspective?
Bibliographic record
Abstract
This research investigates whether the US stock volatility index (S&P 500 index) has the forecasting ability to predict the volatility of CAC index (France), DAX index (Germany), and FTSE index (the UK) by employing a multivariate heterogeneous autoregressive realized volatility jump (MHAR-RV-CJ) model. Our empirical results provide consolidated comparisons using univariate and multivariate models. The in-sample results show us the US volatility will improve the long-term volatility regression coefficient. Moreover, our proposed model, the MHAR-RV-CJ model, nearly surpasses all competing models at out-of-sample forecasting, indicating that considering the multivariate DCC-GARCH information between US-France, US-Germany, and US-UK stock markets and jump component structures can help to predict individual European stock market volatility. Unsurprisingly, several forecasting evaluation tests and further analysis (high/low volatility) confirm the robustness of our results.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".