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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".