Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| GJR-GARCH (Asymetrický GARCH)× | Model ARIMA (autoregresní integrovaný klouzavý průměr)× | Model GARCH (Predikce volatility)× | |
|---|---|---|---|
| Obor | Ekonometrie | Ekonometrie | Ekonometrie |
| Rodina | Regression model | Regression model | Regression model |
| Rok vzniku≠ | 1993 | 2015 | 1986 |
| Tvůrce≠ | Glosten, Jagannathan & Runkle (1993); Zakoian (1994) | Box & Jenkins (Box-Jenkins methodology) | Tim Bollerslev |
| Typ≠ | Asymmetric conditional volatility model | Univariate time-series model | Conditional volatility model |
| Původní zdroj≠ | Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801. 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 | Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗ |
| Další názvy≠ | asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle) | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini) |
| Příbuzné | 5 | 5 | 5 |
| Shrnutí≠ | GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994). | 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). | 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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