手法を比較

	GJR-GARCH（非対称GARCH）	Panel EGARCH	パネルデータ固定効果モデル
分野	計量経済学	計量経済学	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	1993	1991 (EGARCH); panel extensions widely used from 2000s	2014
提唱者	Glosten, Jagannathan & Runkle (1993); Zakoian (1994)	Daniel B. Nelson (EGARCH); panel extension by applied econometrics literature	Hsiao (textbook treatment); within transformation of panel data
種類	Asymmetric conditional volatility model	Volatility model	Panel data regression
原典	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.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
別名	asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)	Panel EGARCH model, panel exponential GARCH, EGARCH for panel data, cross-sectional EGARCH	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
関連	5	4	5
概要	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).	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).