Сравнение методов

	Панельная модель TGARCH (Threshold GARCH для панельных данных)	DCC-GARCH (Dynamic Conditional Correlation)	Панельная модель EGARCH
Область	Эконометрика	Финансы	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1993–1994 (panel extension: 2000s onward)	2002	1991 (EGARCH); panel extensions widely used from 2000s
Автор метода	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
Тип	Asymmetric conditional volatility model	Multivariate volatility model	Volatility model
Основополагающий источник	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.
Другие названия	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
Связанные	4	5	4
Сводка	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.