방법 비교

	Quantile-on-Quantile (QQ) 회귀	DCC-GARCH 모형 (동적 조건부 상관관계)
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	2015	2002
창시자	Sim and Zhou	Robert F. Engle
유형	Nonparametric quantile regression	Multivariate volatility model
원전	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.
별칭	QQ regression, QQ approach, quantile-on-quantile approach, nonparametric quantile regression	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
관련	6	5
요약	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.