Confronta i metodi

	Modello DCC-GARCH Non Lineare (Correlazione Condizionale Dinamica Asimmetrica)	Modello DCC-GARCH (Dynamic Conditional Correlation)	Modello EGARCH (Exponential GARCH)
Campo	Econometria	Econometria	Econometria
Famiglia	Regression model	Regression model	Regression model
Anno di origine	2006	2002	1991
Ideatore	Cappiello, Engle & Sheppard	Robert F. Engle	Daniel B. Nelson
Tipo	Multivariate volatility and correlation model	Multivariate volatility model	Volatility / conditional variance model
Fonte seminale	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.	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.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Alias	ADCC-GARCH, Asymmetric DCC-GARCH, NL-DCC-GARCH, Nonlinear Asymmetric DCC	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
Correlati	2	5	6
Sintesi	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 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.	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.