Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo EGARCH Não Linear× | Modelo TGARCH (GARCH Limiar)× | |
|---|---|---|
| Área | Econometria | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1991 | 1993-1994 |
| Autor original≠ | Daniel B. Nelson | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) |
| Tipo≠ | Conditional volatility model | Asymmetric volatility model |
| Fonte seminal≠ | Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ |
| Outros nomes | NL-EGARCH, nonlinear exponential GARCH, asymmetric EGARCH, NEGARCH | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH |
| Relacionados≠ | 5 | 6 |
| Resumo≠ | 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 Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative. |
| ScholarGateConjunto de dados ↗ |
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