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| Uogólniona warunkowa heteroskedastyczność z autoregresją (GARCH)× | Model ARIMA (Autoregressive Integrated Moving Average)× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 1986 | 2015 |
| Twórca≠ | Tim Bollerslev | Box & Jenkins (Box-Jenkins methodology) |
| Typ≠ | Conditional volatility model | Univariate time-series model |
| Źródło pierwotne≠ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗ | 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 |
| Inne nazwy≠ | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli |
| Pokrewne | 5 | 5 |
| Podsumowanie≠ | GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns. | 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). |
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