Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Modelo GARCH no lineal× | Modelo EGARCH (GARCH Exponencial)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1991-1993 | 1991 |
| Autor original≠ | Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH | Daniel B. Nelson |
| Tipo≠ | Volatility model | Volatility / conditional variance model |
| Fuente seminal≠ | Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801. DOI ↗ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ |
| Alias | NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model | Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH |
| Relacionados | 6 | 6 |
| Resumen≠ | The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets. | 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. |
| ScholarGateConjunto de datos ↗ |
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