Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Model ARCH (Autoregresivní podmíněná heteroskedasticita)× | Model TGARCH (Threshold GARCH)× | |
|---|---|---|
| Obor | Ekonometrie | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 1982 | 1993-1994 |
| Tvůrce≠ | Robert F. Engle | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) |
| Typ≠ | Conditional volatility model | Asymmetric volatility model |
| Původní zdroj≠ | Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. DOI ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ |
| Další názvy | ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH |
| Příbuzné | 6 | 6 |
| Shrnutí≠ | 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. | 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. |
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