So sánh phương pháp

	Mô hình ARIMA phi tuyến tính	Mô hình GARCH (Dự báo Biến động)
Lĩnh vực	Kinh tế lượng	Kinh tế lượng
Họ	Regression model	Regression model
Năm ra đời	1978-1994	1986
Người khởi xướng	Howell Tong (SETAR/TAR framework); Timo Terasvirta (STAR extensions)	Tim Bollerslev
Loại	Nonlinear time series model	Conditional volatility model
Công trình gốc	Tong, H. (1990). Non-Linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 9780198522249	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Tên gọi khác	nonlinear ARIMA, NARIMA, nonlinear time series model, nonlinear Box-Jenkins model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Liên quan	3	5
Tóm tắt	The Nonlinear ARIMA model extends the classical Box-Jenkins ARIMA framework by allowing the conditional mean of a time series to depend on past values and past errors through a nonlinear function. It encompasses families such as Threshold AR (TAR/SETAR), Smooth Transition AR (STAR/LSTAR/ESTAR), and Markov-switching models, capturing asymmetric dynamics, regime changes, and business-cycle asymmetries that linear ARIMA cannot represent.	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.