Σύγκριση μεθόδων

	Μη Γραμμικό Υπόδειγμα GARCH	Μοντέλο DCC-GARCH (Dynamic Conditional Correlation)
Πεδίο	Οικονομετρία	Οικονομετρία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1991-1993	2002
Δημιουργός	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	Robert F. Engle
Τύπος	Volatility model	Multivariate volatility model
Θεμελιώδης πηγή	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. 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.
Εναλλακτικές ονομασίες	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Συναφείς	6	5
Σύνοψη	The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets.	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.