قارن الطرق

	اختبار السببية في التباين	GARCH-MIDAS
المجال	الاقتصاد القياسي	الاقتصاد القياسي
العائلة	Regression model	Regression model
سنة النشأة	1996	2012
صاحب الطريقة	Yin-Wong Cheung and Lilian Ng	Engle and Ghysels
النوع	Conditional variance test	Time-varying variance model
المصدر التأسيسي	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.	Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391.
الأسماء البديلة	Volatility spillover test	Mixed-frequency volatility model
ذات صلة	3	3
الملخص	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.	GARCH-MIDAS decomposes volatility into short-term (GARCH) and long-term (MIDAS) components, allowing low-frequency macroeconomic variables to drive medium-term volatility while high-frequency returns govern daily fluctuations. Introduced by Engle and Ghysels (2012), this framework elegantly separates volatility time scales. The approach is powerful for understanding how macro conditions (growth, inflation) drive risk premia and for improved volatility forecasting.