Compară metode

	Panel TGARCH (Threshold GARCH pentru Date Paneliu)	DCC-GARCH (Dynamic Conditional Correlation)	Panel EGARCH	Modelul cu Efecte Fixe pentru Date Panou
Domeniu	Econometrie	Finanțe	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model	Regression model
Anul apariției	1993–1994 (panel extension: 2000s onward)	2002	1991 (EGARCH); panel extensions widely used from 2000s	2014
Autorul original	Glosten, Jagannathan & Runkle (1993); Zakoian (1994); extended to panel settings by subsequent applied finance literature	Robert F. Engle	Daniel B. Nelson (EGARCH); panel extension by applied econometrics literature	Hsiao (textbook treatment); within transformation of panel data
Tip	Asymmetric conditional volatility model	Multivariate volatility model	Volatility model	Panel data regression
Sursa seminală	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.	Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.	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.
Denumiri alternative	Panel GJR-GARCH, Panel Asymmetric GARCH, Panel Threshold GARCH, TGARCH panel model	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	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
Înrudite	4	5	4	5
Rezumat	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.	DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step.	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).