Performance des fonds de couverture, moments supérieurs et risque procyclique
Bibliographic record
Abstract
L’analyse de la procyclicité du risque est devenue un sujet de recherche très florissant depuis le début de la crise de 2007 mais elle n’a pas été transposée à l’étude du risque des fonds de couverture. Nous innovons en dynamisant les stratégies des fonds par la technique des alphas et bêtas conditionnels. Nous comparons cette technique à une autre de notre cru qui fait appel à des instruments non-linéaires afin de corriger les erreurs de spécification qui pourraient se glisser dans l’estimation de modèles financiers. Nous vérifions que les bêtas des fonds de couverture sont fortement procycliques. Mots-clés : Procyclicité du risque ; Erreurs de spécification ; Moments supérieurs ; Variables instrumentales ; Alpha et bêta conditionnels. Classification JEL : C13 ; C19 ; C49 ; G12 ; G31.
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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".