השוואת שיטות

	DCC-MIDAS	VAR קוונטיל
תחום	אקונומטריקה	אקונומטריקה
משפחה	Regression model	Regression model
שנת המקור	2013	2006
הוגה השיטה	Engle, Ghysels, and Sohn	Koenker and Xiao
סוג	Time-varying correlation model	Distribution impulse response
מקור מכונן	Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics, 95(3), 776-797.	Koenker, R., & Xiao, Z. (2006). Quantile autoregression. Journal of the American Statistical Association, 101(475), 980-990.
כינויים	DCC mixed-frequency model	Quantile-based impulse response
קשורות	3	3
תקציר	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.	Quantile VAR estimates impulse responses of multivariate systems conditional on different quantiles of the distribution, revealing how shocks propagate heterogeneously across the conditional distribution. Introduced by Koenker and Xiao (2006) and applied to risk measurement by White et al. (2015), it reveals tail behavior and contagion effects invisible to mean-based VAR analysis. This is essential for risk management and understanding how crises propagate differently than normal times.