Сравнение на методи

	Нелинеен SARIMA модел	Модел GARCH (Прогнозиране на волатилността)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1990–2000	1986
Създател	Tong (1990) for threshold nonlinear extensions; Franses & van Dijk (2000) for empirical finance applications	Tim Bollerslev
Тип	Nonlinear time series model	Conditional volatility model
Основополагащ източник	Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford University Press. ISBN: 978-0198523000	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Други названия	NL-SARIMA, nonlinear seasonal ARIMA, threshold SARIMA, smooth transition SARIMA	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Свързани	3	5
Резюме	The Nonlinear SARIMA model extends the classical Seasonal ARIMA framework by replacing the linear conditional mean function with a nonlinear specification — such as threshold switching or smooth transition — while retaining seasonal differencing and lag structure. It is used when seasonal time series exhibit regime-dependent dynamics, asymmetric adjustment, or other nonlinear patterns that a linear model cannot capture.	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.