Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Exponential GARCH (EGARCH)× | Modelos de Memória Longa (ARFIMA, FIGARCH)× | |
|---|---|---|
| Área≠ | Econometria | Finanças |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1991 | 1980 |
| Autor original≠ | Nelson | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) |
| Tipo≠ | Conditional volatility model (asymmetric GARCH variant) | Fractionally integrated time series model |
| Fonte seminal≠ | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. DOI ↗ |
| Outros nomes≠ | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | ARFIMA, FIGARCH, fractionally integrated models, fractional integration |
| Relacionados | 4 | 4 |
| Resumo≠ | EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance. | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. |
| ScholarGateConjunto de dados ↗ |
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