Сравнение на методи

	Квантил-върху-квантилна (КвКв) регресия	DCC-GARCH модел (динамична условна корелация)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2015	2002
Създател	Sim and Zhou	Robert F. Engle
Тип	Nonparametric quantile regression	Multivariate volatility model
Основополагащ източник	Sim, N., & Zhou, H. (2015). Oil prices, US stock return, and the dependence between their quantiles. Journal of Banking and Finance, 55, 1-8.	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.
Други названия	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Свързани	6	5
Резюме	Quantile-on-quantile regression is a nonparametric technique that estimates how the quantiles of one variable depend on the quantiles of another. By combining standard quantile regression with local linear smoothing, it produces a full two-dimensional surface of slope coefficients indexed by both the quantile of the outcome and the quantile of the predictor, revealing heterogeneous and asymmetric dependency structures invisible to standard regression.	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.