পদ্ধতির তুলনা করুন

	DCC-GARCH (Dynamic Conditional Correlation)	ARIMA (Autoregressive Integrated Moving Average) মডেল	এক্সপোনেনশিয়াল GARCH (EGARCH)	চরম মান তত্ত্ব (Extreme Value Theory - EVT)
ক্ষেত্র	অর্থায়ন	অর্থমিতি	অর্থমিতি	অর্থায়ন
পরিবার	Regression model	Regression model	Regression model	Regression model
উদ্ভবের বছর	2002	2015	1991	2001
প্রবর্তক	Robert F. Engle	Box & Jenkins (Box-Jenkins methodology)	Nelson	Coles (textbook treatment); McNeil, Frey & Embrechts
ধরন	Multivariate volatility model	Univariate time-series model	Conditional volatility model (asymmetric GARCH variant)	Tail / extreme-event model
মৌলিক উৎস	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	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. Springer. ISBN: 978-1852334598
অপর নাম	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	EVT, generalized extreme value, generalized Pareto distribution, peaks over threshold
সম্পর্কিত	5	5	4	5
সারসংক্ষেপ	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).	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	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.