So sánh phương pháp

	Mô hình GARCH (Dự báo Biến động)	Mô hình ARIMA (Autoregressive Integrated Moving Average)	Làm mịn hàm mũ đơn và kép (SES / Holt)	Hồi quy Bình phương Tối thiểu Thông thường (OLS)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model	Regression model	Regression model
Năm ra đời	1986	2015	1957	2019
Người khởi xướng	Tim Bollerslev	Box & Jenkins (Box-Jenkins methodology)	Robert G. Brown (SES); Charles C. Holt (linear trend)	Wooldridge (textbook treatment); classical least squares
Loại	Conditional volatility model	Univariate time-series model	Exponential smoothing forecasting model	Linear regression
Công trình gốc	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	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	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Tên gọi khác	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liên quan	5	5	3	5
Tóm tắt	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.	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).	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).