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| Model Robust EGARCH× | Model DCC-GARCH (Dynamic Conditional Correlation)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2008 | 2002 |
| Twórca≠ | Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors | Robert F. Engle |
| Typ≠ | Robust volatility model | Multivariate volatility model |
| Źródło pierwotne≠ | Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940. DOI ↗ | 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 ↗ |
| Inne nazwy | Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH | DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect. | 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. |
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