Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модели копул (Гауссовы, t, Клейтона, Гумбеля, Франка)× | Обобщенная авторегрессионная условная гетероскедастичность (GARCH)× | |
|---|---|---|
| Область≠ | Финансы | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1959 | 1986 |
| Автор метода≠ | Sklar (1959); dependence-concept treatment by Joe (1997) | Tim Bollerslev |
| Тип≠ | Dependence model | Conditional volatility model |
| Основополагающий источник≠ | 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 ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗ |
| Другие названия | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns. |
| ScholarGateНабор данных ↗ |
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