Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Модель ARCH (авторегрессионная условная гетероскедастичность)× | Модель EGARCH (Экспоненциальная GARCH)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1982 | 1991 |
| Автор метода≠ | Robert F. Engle | Daniel B. Nelson |
| Тип≠ | Conditional volatility model | Volatility / conditional variance model |
| Основополагающий источник≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Другие названия | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Связанные | 6 | 6 |
| Сводка≠ | 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. | The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets. |
| ScholarGateНабор данных ↗ |
|
|