Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Model GARCH Robust× | Model EGARCH (Exponential GARCH)× | |
|---|---|---|
| Domeniu | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 1986–2013 | 1991 |
| Autorul original≠ | Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986) | Daniel B. Nelson |
| Tip≠ | Volatility model | Volatility / conditional variance model |
| Sursa seminală≠ | Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Denumiri alternative | Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Înrudite≠ | 5 | 6 |
| Rezumat≠ | The Robust GARCH model extends the classical GARCH framework to handle outliers and heavy-tailed innovations that commonly appear in financial return series. By down-weighting extreme observations through a robust innovation term, it produces more reliable volatility forecasts when data contain jumps, crises, or other anomalies that would otherwise distort standard GARCH estimates. | 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. |
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