Salīdzināt metodes

	Komponentu GARCH	DCC-MIDAS (dinamiskā nosacītā korelācija ar jauktām frekvencēm)	GARCH-MIDAS
Nozare	Ekonometrija	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model
Izcelsmes gads	1999	2013	2012
Autors	Engle and Lee	Engle, Ghysels, and Sohn	Engle and Ghysels
Tips	Decomposed variance model	Time-varying correlation model	Time-varying variance model
Pirmavots	Engle, R. F., & Lee, G. (1999). A permanent and transitory component model of stock return volatility. Journal of Political Economy, 107(6), 1363-1384.	Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.	Engle, R. F., & Ghysels, E. (2012). GARCH for long memory. Journal of Econometrics, 164(2), 385-391.
Citi nosaukumi	Volatility components model	DCC mixed-frequency model	Mixed-frequency volatility model
Saistītās	3	3	3
Kopsavilkums	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.	DCC-MIDAS combines dynamic conditional correlation (DCC) GARCH with mixed-frequency data sampling (MIDAS), enabling estimation of time-varying correlations between variables when observations arrive at different frequencies. Introduced by Engle et al. (2013), it models how correlations evolve with low-frequency macroeconomic conditions using high-frequency asset price information. This is crucial for portfolio risk management and understanding macro-finance linkages.	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.