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GJR-GARCH (Asymetrický GARCH)×Panel EGARCH×Model panelových dat s fixními efekty×
OborEkonometrieEkonometrieEkonometrie
RodinaRegression modelRegression modelRegression model
Rok vzniku19931991 (EGARCH); panel extensions widely used from 2000s2014
TvůrceGlosten, Jagannathan & Runkle (1993); Zakoian (1994)Daniel B. Nelson (EGARCH); panel extension by applied econometrics literatureHsiao (textbook treatment); within transformation of panel data
TypAsymmetric conditional volatility modelVolatility modelPanel data regression
Původní zdrojGlosten, 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 ↗
Další názvyasymmetric 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
Příbuzné545
ShrnutíGJR-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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ScholarGatePorovnat metody: GJR-GARCH · Panel EGARCH · Panel Fixed Effects. Získáno 2026-06-19 z https://scholargate.app/cs/compare