手法を比較

	パネルTGARCH（パネルデータ用閾値GARCH）	DCC-GARCH（動的条件付き相関）	パネルデータ固定効果モデル
分野	計量経済学	ファイナンス	計量経済学
系統	Regression model	Regression model	Regression model
提唱年	1993–1994 (panel extension: 2000s onward)	2002	2014
提唱者	Glosten, Jagannathan & Runkle (1993); Zakoian (1994); extended to panel settings by subsequent applied finance literature	Robert F. Engle	Hsiao (textbook treatment); within transformation of panel data
種類	Asymmetric conditional volatility model	Multivariate 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. 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.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
別名	Panel GJR-GARCH, Panel Asymmetric GARCH, Panel Threshold GARCH, TGARCH panel model	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
関連	4	5	5
概要	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.	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).