Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Model DCC-GARCH (Dynamic Conditional Correlation)× | Model EGARCH (Exponential GARCH)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2002 | 1991 |
| Twórca≠ | Robert F. Engle | Daniel B. Nelson |
| Typ≠ | Multivariate volatility model | Volatility / conditional variance model |
| Źródło pierwotne≠ | 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 ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Inne nazwy | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Pokrewne≠ | 5 | 6 |
| Podsumowanie≠ | 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 Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets. |
| ScholarGateZbiór danych ↗ |
|
|