Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель DCC-GARCH (динамічна умовна кореляція)× | Модель GARCH (Прогнозування волатильності)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2002 | 1986 |
| Автор методу≠ | Robert F. Engle | Tim Bollerslev |
| Тип≠ | Multivariate volatility model | Conditional volatility model |
| Основоположне джерело≠ | Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Інші назви | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Пов'язані | 5 | 5 |
| Підсумок≠ | The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series. | The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. |
| ScholarGateНабір даних ↗ |
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