手法を比較

	非線形DCC-GARCHモデル（非対称動的条件付き相関）	EGARCHモデル（指数型GARCH）
分野	計量経済学	計量経済学
系統	Regression model	Regression model
提唱年	2006	1991
提唱者	Cappiello, Engle & Sheppard	Daniel B. Nelson
種類	Multivariate volatility and correlation model	Volatility / conditional variance model
原典	Cappiello, L., Engle, R. F., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4(4), 537–572.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
別名	ADCC-GARCH, Asymmetric DCC-GARCH, NL-DCC-GARCH, Nonlinear Asymmetric DCC	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
関連	2	6
概要	The Nonlinear DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by allowing correlations to respond asymmetrically to negative versus positive return shocks. Proposed by Cappiello, Engle, and Sheppard (2006), it is the standard tool for measuring time-varying co-movement and contagion effects in multivariate financial time series when bad news is expected to increase correlations more than good news.	The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.