Compara mètodes

	Regressió Quantil-sobre-Quantil (QQ)	Model DCC-GARCH (Dynamic Conditional Correlation)
Camp	Econometria	Econometria
Família	Regression model	Regression model
Any d'origen	2015	2002
Autor original	Sim and Zhou	Robert F. Engle
Tipus	Nonparametric quantile regression	Multivariate volatility model
Font seminal	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.
Àlies	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Relacionats	6	5
Resum	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.