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GJR-GARCH (GARCH asimmetrico)×Panel EGARCH×Modello a Effetti Fissi per Dati Panel×
CampoEconometriaEconometriaEconometria
FamigliaRegression modelRegression modelRegression model
Anno di origine19931991 (EGARCH); panel extensions widely used from 2000s2014
IdeatoreGlosten, Jagannathan & Runkle (1993); Zakoian (1994)Daniel B. Nelson (EGARCH); panel extension by applied econometrics literatureHsiao (textbook treatment); within transformation of panel data
TipoAsymmetric conditional volatility modelVolatility modelPanel data regression
Fonte seminaleGlosten, 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 ↗Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗
Aliasasymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)Panel EGARCH model, panel exponential GARCH, EGARCH for panel data, cross-sectional EGARCHfixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Correlati545
SintesiGJR-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).Panel EGARCH extends Nelson's (1991) Exponential GARCH model to a panel setting, allowing conditional variance to evolve asymmetrically over time for each cross-sectional unit. The log specification ensures non-negative variance without parameter constraints, and the leverage term distinguishes whether negative shocks amplify volatility more than positive ones of equal magnitude.The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).
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ScholarGateConfronta i metodi: GJR-GARCH · Panel EGARCH · Panel Fixed Effects. Consultato il 2026-06-19 da https://scholargate.app/it/compare