Comparer des méthodes

	GARCH à Composantes	Test de causalité dans la variance
Domaine	Économétrie	Économétrie
Famille	Regression model	Regression model
Année d'origine	1999	1996
Auteur d'origine	Engle and Lee	Yin-Wong Cheung and Lilian Ng
Type	Decomposed variance model	Conditional variance test
Source fondatrice	Engle, R. F., & Lee, G. (1999). A permanent and transitory component model of stock return volatility. Journal of Political Economy, 107(6), 1363-1384.	Cheung, Y. W., & Ng, L. K. (1996). A causality-in-variance test and its application to financial market prices. Journal of Econometrics, 72(1-2), 33-61.
Alias	Volatility components model	Volatility spillover test
Apparentées	3	3
Résumé	Component GARCH decomposes conditional variance into transitory (short-term) and permanent (long-term) components with different dynamics, allowing flexibility in capturing volatility behavior at multiple frequencies. Introduced by Engle and Lee (1999), it elegantly models the empirical finding that volatility exhibits both rapid mean-reversion (daily shocks) and slow mean-reversion (level shifts). This framework is crucial for understanding volatility persistence and improving long-horizon volatility forecasting.	The causality-in-variance test detects whether shocks to one variable cause changes in the conditional variance (volatility) of another variable, distinct from mean-level causality. Introduced by Cheung and Ng (1996), it identifies volatility spillovers and contagion effects—crucial for risk management and understanding financial market interdependencies. This approach has become standard in studying shock transmission across asset classes and geographies.