Порівняння методів
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| Модель Robust GARCH× | Модель GARCH (Прогнозування волатильності)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1986–2013 | 1986 |
| Автор методу≠ | Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986) | Tim Bollerslev |
| Тип≠ | Volatility model | Conditional volatility model |
| Основоположне джерело≠ | Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Інші назви | Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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 Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series. |
| ScholarGateНабір даних ↗ |
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