Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kopuļu modeļi (Gausa, t, Clayton, Gumbel, Frank)× | Generalizētā autoregresīvā nosacītā heteroskedastiskuma (GARCH) modelis× | |
|---|---|---|
| Nozare≠ | Finanses | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1959 | 1986 |
| Autors≠ | Sklar (1959); dependence-concept treatment by Joe (1997) | Tim Bollerslev |
| Tips≠ | Dependence model | Conditional volatility model |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
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