השוואת שיטות

	DCC-GARCH (Dynamic Conditional Correlation)	Exponential GARCH (EGARCH)	תורת הערכים הקיצוניים (EVT)
תחום	מימון	אקונומטריקה	מימון
משפחה	Regression model	Regression model	Regression model
שנת המקור	2002	1991	2001
הוגה השיטה	Robert F. Engle	Nelson	Coles (textbook treatment); McNeil, Frey & Embrechts
סוג	Multivariate volatility model	Conditional volatility model (asymmetric GARCH variant)	Tail / extreme-event model
מקור מכונן	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.	Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598
כינויים	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold
קשורות	5	4	5
תקציר	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.	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold.