Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Обобщенная авторегрессионная условная гетероскедастичность (GARCH)× | DCC-GARCH (Dynamic Conditional Correlation)× | |
|---|---|---|
| Область≠ | Эконометрика | Финансы |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1986 | 2002 |
| Автор метода≠ | Tim Bollerslev | Robert F. Engle |
| Тип≠ | Conditional volatility model | Multivariate volatility model |
| Основополагающий источник≠ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗ | Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350. DOI ↗ |
| Другие названия | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli | dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon |
| Связанные | 5 | 5 |
| Сводка≠ | 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. | DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step. |
| ScholarGateНабор данных ↗ |
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