Efficient Estimation of Conditional Asset Pricing Models
Bibliographic record
Abstract
A semiparametric efficient estimation procedure is developed for the parameters of multivariate GARCH-in-mean models when the disturbances have a distribution that is assumed to be elliptically symmetric but is otherwise unrestricted. Under high level restrictions, the resulting estimator achieves the asymptotic semiparametric efficiency bound. The elliptical symmetry assumption allows us to avert the curse of dimensionality problem that would otherwise arise in estimating the unknown error distribution. This framework is suitable for the estimation and testing of conditional asset pricing models such as the conditional CAPM, and we apply our estimator in an empirical study of stock prices, with Monte Carlo simulation results also being reported. Nous développons un nouvel estimateur pour les paramètres d'un modèle de GARCH en moyenne (" GARCH-M ") avec plusieurs variables. L'estimateur a l'efficacité semiparamétrique quand les erreurs suivent une loi de probabilité qui est elliptiquement symétrique mais n'aucune autre restriction. Sous les hypothèses de haut niveau, notre estimateur obtient la limite d'efficacité semiparamétrique. L'hypothèse de la symétrie elliptique nous permet d'éviter le problème d'estimer non-paramétriquement une fonction de haut dimension, parce qu'on peut écrire la densité d'un loi elliptique comme un fonction d'une transformation unidimensionnelle de la variable aléatoire multidimensionnelle. Ce cadre est approprié pour analyser des modèles conditionnels des prix des actifs financiers, comme le CAPM conditionnel. Nous appliquons notre méthodologie à l'étude des prix des actions, et nous rendons compte des résultats d'une étude simulation "Monte-Carlo".
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 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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".