Сравнение на методи
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| Модел EGARCH (Експоненциален GARCH)× | Модел ARCH (Авторегресивен условен хетероскедастичност)× | |
|---|---|---|
| Област | Иконометрия | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1991 | 1982 |
| Създател≠ | Daniel B. Nelson | Robert F. Engle |
| Тип≠ | Volatility / conditional variance model | Conditional volatility model |
| Основополагащ източник≠ | 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 ↗ |
| Други названия | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Свързани | 6 | 6 |
| Резюме≠ | 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. | 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 от данни ↗ |
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