手法を比較

	TGARCHモデル（Threshold GARCH）	ARCHモデル（Autoregressive Conditional Heteroskedasticity）	DCC-GARCHモデル（動学的条件付き相関）
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	1993-1994	1982	2002
提唱者	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)	Robert F. Engle	Robert F. Engle
種類	Asymmetric volatility model	Conditional volatility model	Multivariate volatility model
原典	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.
別名	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
関連	6	6	5
概要	The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative.	The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.	The DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.