Testing and Comparing Value-at-Risk Measures
Bibliographic record
Abstract
La valeur expose au risque (value at risk - VaR) est devenue un outil standard de mesure et de communication des risques associs aux marchs financiers. Plus de quatre-vingts fournisseurs commerciaux proposent actuellement des systmes de gestion d'entreprise ou de gestion des risques commerciaux fournissant des mesures de type VaR. C'est donc souvent aux gestionnaires des risques qu'incombe la tche difficile d'oprer un choix parmi cette plthore de modles de risques. Cet article propose un cadre utile pour dterminer par quel moyen le gestionnaire des risques peut s'assurer que la mesure de VaR dont il dispose est bien dfinie, et, dans un deuxime temps, comparer deux mesures de VaR diffrentes et choisir la meilleure en s'appuyant sur des donnes statistiques utiles. Dans l'application, diffrentes mesures de VaR sont calcules partir soit de mesures de volatilit historiques ou de mesures de volatilit implicites dans le prix des options; les VaR sont galement vrifies et compares. Value-at-R...
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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 | 0.058 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".