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| 강건 EGARCH 모형× | 강건 GARCH 모형× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2008 | 1986–2013 |
| 창시자≠ | Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors | Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986) |
| 유형≠ | Robust volatility model | Volatility model |
| 원전≠ | 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 ↗ |
| 별칭 | Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH | Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model |
| 관련≠ | 6 | 5 |
| 요약≠ | 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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