পদ্ধতির তুলনা করুন

	ইগার্চ মডেল (এক্সপোনেনশিয়াল GARCH)	ARIMA মডেল (অটোরিগ্রেসিভ ইন্টিগ্রেটেড মুভিং অ্যাভারেজ)	ডিসি-জিএআরসিএইচ মডেল (ডাইনামিক কন্ডিশনাল কোরিলেশন)
ক্ষেত্র	অর্থমিতি	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model	Regression model
উদ্ভবের বছর	1991	1970	2002
প্রবর্তক	Daniel B. Nelson	George Box and Gwilym Jenkins	Robert F. Engle
ধরন	Volatility / conditional variance model	Time series forecasting model	Multivariate volatility model
মৌলিক উৎস	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	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.
অপর নাম	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
সম্পর্কিত	6	6	5
সারসংক্ষেপ	The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.	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.