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| Model ARIMA (Autoregressive Integrated Moving Average)× | Eksponencijalni GARCH (EGARCH)× | Generalizovana autoregresivna uslovna heteroskedastičnost (GARCH)× | |
|---|---|---|---|
| Oblast | Ekonometrija | Ekonometrija | Ekonometrija |
| Porodica | Regression model | Regression model | Regression model |
| Godina nastanka≠ | 2015 | 1991 | 1986 |
| Tvorac≠ | Box & Jenkins (Box-Jenkins methodology) | Nelson | Tim Bollerslev |
| Tip≠ | Univariate time-series model | Conditional volatility model (asymmetric GARCH variant) | Conditional volatility model |
| Temeljni izvor≠ | 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 | Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗ | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗ |
| Drugi nazivi≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli |
| Srodne≠ | 5 | 4 | 5 |
| Sažetak≠ | 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). | EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance. | 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. |
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