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| Wykładniczy GARCH (EGARCH)× | GJR-GARCH (Asymetryczny GARCH)× | TBATS× | |
|---|---|---|---|
| Dziedzina | Ekonometria | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model | Regression model |
| Rok powstania≠ | 1991 | 1993 | 2011 |
| Twórca≠ | Nelson | Glosten, Jagannathan & Runkle (1993); Zakoian (1994) | De Livera, Hyndman & Snyder |
| Typ≠ | Conditional volatility model (asymmetric GARCH variant) | Asymmetric conditional volatility model | Exponential smoothing state space model |
| Źródło pierwotne≠ | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ | 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. The Journal of Finance, 48(5), 1779-1801. DOI ↗ | De Livera, A. M., Hyndman, R. J. & Snyder, R. D. (2011). Forecasting Time Series with Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), 1513-1527. DOI ↗ |
| Inne nazwy | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle) | trigonometric exponential smoothing, multiple seasonal exponential smoothing, complex seasonal exponential smoothing, TBATS — Çoklu Mevsimsel Üstel Düzleştirme |
| Pokrewne≠ | 4 | 5 | 3 |
| Podsumowanie≠ | 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. | GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994). | TBATS is an innovations state space forecasting model, introduced by De Livera, Hyndman and Snyder (2011), that combines a Box-Cox transformation, ARMA errors and trigonometric (Fourier) seasonal terms. It is built to handle continuous time series with several nested seasonal cycles at once — for example hourly data that also repeats daily, weekly and yearly. |
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