Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Нелинейная модель EGARCH× | Модель ARCH (авторегрессионная условная гетероскедастичность)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1991 | 1982 |
| Автор метода≠ | Daniel B. Nelson | Robert F. Engle |
| Тип | Conditional volatility model | Conditional volatility model |
| Основополагающий источник≠ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| Другие названия | NL-EGARCH, nonlinear exponential GARCH, asymmetric EGARCH, NEGARCH | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Связанные≠ | 5 | 6 |
| Сводка≠ | The Nonlinear EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the news impact function to take a flexible nonlinear form, capturing asymmetric and nonlinear responses of conditional volatility to past shocks. It is widely used in financial econometrics to model leverage effects and complex volatility dynamics in asset returns. | The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering. |
| ScholarGateНабор данных ↗ |
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