Comparar métodos

	Modelo SARIMA Não Linear	Modelo GARCH (Previsão de Volatilidade)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1990–2000	1986
Autor original	Tong (1990) for threshold nonlinear extensions; Franses & van Dijk (2000) for empirical finance applications	Tim Bollerslev
Tipo	Nonlinear time series model	Conditional volatility model
Fonte seminal	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.
Outros nomes	NL-SARIMA, nonlinear seasonal ARIMA, threshold SARIMA, smooth transition SARIMA	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Relacionados	3	5
Resumo	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.