So sánh phương pháp

	Mô hình EGARCH (Exponential GARCH)	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Mô hình DCC-GARCH (Tương quan có điều kiện động)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model	Regression model
Năm ra đời	1991	1970	2002
Người khởi xướng	Daniel B. Nelson	George Box and Gwilym Jenkins	Robert F. Engle
Loại	Volatility / conditional variance model	Time series forecasting model	Multivariate volatility model
Công trình gốc	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.
Tên gọi khác	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
Liên quan	6	6	5
Tóm tắt	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.