Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модели долгой памяти (ARFIMA, FIGARCH)× | Модель GARCH (прогнозирование волатильности)× | |
|---|---|---|
| Область≠ | Финансы | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1980 | 1986 |
| Автор метода≠ | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) | Tim Bollerslev |
| Тип≠ | Fractionally integrated time series model | Conditional volatility model |
| Основополагающий источник≠ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Другие названия≠ | ARFIMA, FIGARCH, fractionally integrated models, fractional integration | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Связанные≠ | 4 | 5 |
| Сводка≠ | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. | 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 данных ↗ |
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