Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Generalizētā autoregresīvā nosacītā heteroskedastiskuma (GARCH) modelis× | SARIMA (Seasonālais ARIMA)× | |
|---|---|---|
| Nozare | Ekonometrija | Ekonometrija |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1986 | 2015 |
| Autors≠ | Tim Bollerslev | Box & Jenkins (seasonal extension of ARIMA) |
| Tips≠ | Conditional volatility model | Seasonal time-series model |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli | seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period. |
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