방법 비교

	DCC-GARCH (동적 조건부 상관관계)	ARIMA (Autoregressive Integrated Moving Average) 모형	극단값 이론 (Extreme Value Theory, EVT)
분야	재무학	계량경제학	재무학
계열	Regression model	Regression model	Regression model
기원 연도	2002	2015	2001
창시자	Robert F. Engle	Box & Jenkins (Box-Jenkins methodology)	Coles (textbook treatment); McNeil, Frey & Embrechts
유형	Multivariate volatility model	Univariate time-series model	Tail / extreme-event model
원전	Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598
별칭	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold
관련	5	5	5
요약	DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	Extreme Value Theory is a statistical framework for modelling the rare events that live in the tail of a probability distribution. As developed in Coles (2001) and applied to risk by McNeil, Frey & Embrechts (2005), it offers two standard routes: the Generalized Extreme Value (GEV) distribution for block maxima and the Generalized Pareto Distribution (GPD), used in the peaks-over-threshold approach, for exceedances above a high threshold.