Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| TGARCH Panel (GARCH de Umbral para Datos de Panel)× | GJR-GARCH (GARCH asimétrico)× | |
|---|---|---|
| Campo | Econometría | Econometría |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1993–1994 (panel extension: 2000s onward) | 1993 |
| Autor original≠ | Glosten, Jagannathan & Runkle (1993); Zakoian (1994); extended to panel settings by subsequent applied finance literature | Glosten, Jagannathan & Runkle (1993); Zakoian (1994) |
| Tipo | Asymmetric conditional volatility model | Asymmetric conditional volatility model |
| Fuente seminal≠ | 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. Journal of Finance, 48(5), 1779–1801. 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 ↗ |
| Alias | Panel GJR-GARCH, Panel Asymmetric GARCH, Panel Threshold GARCH, TGARCH panel model | asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle) |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | Panel TGARCH extends the Threshold GARCH (GJR-GARCH) model to panel data, allowing each cross-sectional unit to exhibit asymmetric volatility responses — where negative shocks generate larger variance increases than positive shocks of the same magnitude — while exploiting the cross-sectional dimension to obtain more efficient parameter estimates. | 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). |
| ScholarGateConjunto de datos ↗ |
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