Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo EGARCH No Lineal× | Modelo ARCH (Heterocedasticidad Autoregresiva Condicional)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1991 | 1982 |
| Autor original≠ | Daniel B. Nelson | Robert F. Engle |
| Tipo | Conditional volatility model | Conditional volatility model |
| Fuente seminal≠ | 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 ↗ |
| Alias | NL-EGARCH, nonlinear exponential GARCH, asymmetric EGARCH, NEGARCH | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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