Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Structural Break TGARCH× | TGARCH modelis (sliekšņa GARCH)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1990-1993 | 1993-1994 |
| Autors≠ | Lamoureux & Lastrapes (structural breaks in GARCH); Glosten, Jagannathan & Runkle (TGARCH/GJR-GARCH asymmetry) | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) |
| Tips≠ | Volatility model | Asymmetric volatility model |
| Pirmavots≠ | Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business & Economic Statistics, 8(2), 225-234. DOI ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ |
| Citi nosaukumi | SB-TGARCH, threshold GARCH with structural breaks, GJR-GARCH with structural breaks, break-adjusted TGARCH | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH |
| Saistītās≠ | 3 | 6 |
| Kopsavilkums≠ | Structural Break TGARCH extends the Threshold GARCH (GJR-GARCH) model to accommodate discrete, permanent shifts in the volatility process. By detecting structural breaks and incorporating them — either as regime-specific intercepts or dummy variables — the model separates genuine volatility persistence from spurious persistence induced by ignored regime changes, and preserves the asymmetric leverage effect that characterises equity and financial return data. | 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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