Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Panel GARCH-model× | TGARCH-model (Threshold GARCH)× | |
|---|---|---|
| Vakgebied | Econometrie | Econometrie |
| Familie | Regression model | Regression model |
| Jaar van ontstaan≠ | 1986 (GARCH); panel extension 1990s–2000s | 1993-1994 |
| Grondlegger≠ | Bollerslev (1986); extended to panel settings in subsequent literature | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) |
| Type≠ | Volatility model | Asymmetric volatility model |
| Oorspronkelijke bron≠ | Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ |
| Aliassen | panel GARCH, GARCH panel model, panel volatility model, panel conditional heteroscedasticity model | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH |
| Verwant | 6 | 6 |
| Samenvatting≠ | The Panel GARCH model extends Bollerslev's (1986) Generalized Autoregressive Conditional Heteroscedasticity framework to panel data, allowing conditional variance to evolve over time for each cross-sectional unit. It simultaneously captures unit-level heterogeneity and time-varying volatility clustering, making it the standard tool for modelling risk and uncertainty in multi-entity financial and macroeconomic panels. | 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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