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| Robust EGARCH-modell× | Robust GARCH-modell× | |
|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 2008 | 1986–2013 |
| Upphovsperson≠ | Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors | Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986) |
| Typ≠ | Robust volatility model | Volatility model |
| Ursprungskälla≠ | Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940. DOI ↗ | Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257. DOI ↗ |
| Alias | Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH | Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | 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 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. |
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