La performance et le conservatisme des modèles VAR mensuelle
Bibliographic record
Abstract
Cette étude compare quatorze modèles de Valeur à risque (ci-après VAR) mensuelle des marchés boursiers canadiens et américains dans l’optique d’un gestionnaire de portefeuille institutionnel. Notre analyse se concentre sur l’importance de quatre caractéristiques des modèles VAR par simulations historiques avec filtre, une des approches les plus prometteuses. Nos résultats montrent que les modèles VAR mensuelle par simulations historiques avec un filtre quotidien de type GARCH sont les seuls à ne pas être rejetés à l’égard de tous les tests de performance effectués. Parmi ces modèles, la spécification GARCH asymétrique, qui s’avère la plus conservatrice, indique que les indices S&P/TSX Composite et S&P500 ont une probabilité de 5 % d’une perte moyenne respective d’au moins 7,4 % et 6,7 % de leur valeur sur un mois.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".