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| Model TGARCH (Threshold GARCH)× | Model ARCH (Autoregresywna Heteroskedastyczność Warunkowa)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1993-1994 | 1982 |
| Twórca≠ | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) | Robert F. Engle |
| Typ≠ | Asymmetric volatility model | Conditional volatility model |
| Źródło pierwotne≠ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ |
| Inne nazwy | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model |
| Pokrewne | 6 | 6 |
| Podsumowanie≠ | 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. | 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. |
| ScholarGateZbiór danych ↗ |
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