Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Экспоненциальный GARCH (EGARCH)× | Реализованная волатильность и модель HAR× | |
|---|---|---|
| Область≠ | Эконометрика | Финансы |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1991 | 2009 |
| Автор метода≠ | Nelson | Corsi (HAR model); Andersen, Bollerslev, Diebold & Labys (realized volatility) |
| Тип≠ | Conditional volatility model (asymmetric GARCH variant) | Time-series regression of realized variance |
| Основополагающий источник≠ | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ | Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. DOI ↗ |
| Другие названия≠ | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | realized variance, HAR model, heterogeneous autoregressive model of realized volatility, HAR-RV |
| Связанные≠ | 4 | 5 |
| Сводка≠ | EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance. | Realized volatility estimates an asset's variance directly from high-frequency intraday returns rather than from a parametric latent process. The Heterogeneous Autoregressive (HAR) model of Corsi (2009), building on the realized-volatility framework of Andersen, Bollerslev, Diebold and Labys (2003), forecasts this measure by combining daily, weekly, and monthly volatility components, and is a strong alternative to GARCH for volatility prediction. |
| ScholarGateНабор данных ↗ |
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