Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Модель GARCH з параметрами, що змінюються в часі (TVP-GARCH)× | Модель GARCH (Прогнозування волатильності)× | |
|---|---|---|
| Галузь | Економетрика | Економетрика |
| Родина | Regression model | Regression model |
| Рік появи≠ | 1982–2013 | 1986 |
| Автор методу≠ | Engle (1982) for ARCH/GARCH foundation; extended by Creal, Koopman & Lucas (2013) and others for time-varying parameter variants | Tim Bollerslev |
| Тип≠ | Volatility model with time-varying coefficients | Conditional volatility model |
| Основоположне джерело≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Інші назви | TVP-GARCH, time-varying GARCH, TV-GARCH, state-space GARCH | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Пов'язані | 5 | 5 |
| Підсумок≠ | The Time-Varying Parameter GARCH model extends the standard GARCH framework by allowing the conditional variance parameters — including the ARCH and GARCH coefficients — to change over time rather than remaining fixed throughout the sample. This makes it well-suited to financial and macroeconomic series where volatility dynamics evolve across different market regimes or economic episodes. | 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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