Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Modèles de copules (Gaussienne, t, Clayton, Gumbel, Frank)× | Exponential GARCH (EGARCH)× | |
|---|---|---|
| Domaine≠ | Finance | Économétrie |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1959 | 1991 |
| Auteur d'origine≠ | Sklar (1959); dependence-concept treatment by Joe (1997) | Nelson |
| Type≠ | Dependence model | Conditional volatility model (asymmetric GARCH variant) |
| Source fondatrice≠ | Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut Statistique de l'Université de Paris, 8, 229-231. link ↗ | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ |
| Alias | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Copula models are a family of functions that describe the dependence structure between variables separately from their individual (marginal) distributions. The foundation is Sklar's theorem (1959), which shows that any multivariate distribution can be split into its marginals plus a copula; Joe (1997) developed the modern catalogue of dependence concepts. They are central to portfolio risk and credit modelling. | EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance. |
| ScholarGateJeu de données ↗ |
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