Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел на EGARCH с променливи във времето параметри× | Модел GARCH (Прогнозиране на волатилността)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1991–2000s | 1986 |
| Създател≠ | Nelson (1991) for EGARCH; TVP extension developed across the 1990s–2000s literature (e.g., Harvey, Engle and co-authors) | Tim Bollerslev |
| Тип | Conditional volatility model | Conditional volatility model |
| Основополагащ източник≠ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Други названия | TVP-EGARCH, time-varying EGARCH, EGARCH with time-varying parameters, dynamic parameter EGARCH | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Свързани≠ | 3 | 5 |
| Резюме≠ | The TVP-EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the volatility equation's parameters — including the leverage effect coefficient — to drift continuously over time. This makes it possible to capture structural change and regime evolution in financial return volatility without imposing a fixed break date. | 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. |
| ScholarGateНабор от данни ↗ |
|
|