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| Model nieliniowy SARIMA× | Model GARCH (Prognozowanie zmienności)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1990–2000 | 1986 |
| Twórca≠ | Tong (1990) for threshold nonlinear extensions; Franses & van Dijk (2000) for empirical finance applications | Tim Bollerslev |
| Typ≠ | Nonlinear time series model | Conditional volatility model |
| Źródło pierwotne≠ | 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. DOI ↗ |
| Inne nazwy | NL-SARIMA, nonlinear seasonal ARIMA, threshold SARIMA, smooth transition SARIMA | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Pokrewne≠ | 3 | 5 |
| Podsumowanie≠ | 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. |
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