方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 高斯、t、Clayton、Gumbel、Frank 联结模型× | 广义自回归条件异方差模型 (GARCH)× | |
|---|---|---|
| 领域≠ | 金融学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 1959 | 1986 |
| 提出者≠ | Sklar (1959); dependence-concept treatment by Joe (1997) | Tim Bollerslev |
| 类型≠ | Dependence model | Conditional volatility model |
| 开创性文献≠ | 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 ↗ |
| 别名 | copulas, dependence copulas, vine copulas, Kopula Modelleri (Gaussian, t, Clayton, Gumbel, Frank) | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli |
| 相关 | 5 | 5 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|