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| Modello ARIMA (Autoregressive Integrated Moving Average)× | Eteroschedasticità Condizionale Autoregressiva Generalizzata (GARCH)× | |
|---|---|---|
| Campo | Econometria | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2015 | 1986 |
| Ideatore≠ | Box & Jenkins (Box-Jenkins methodology) | Tim Bollerslev |
| Tipo≠ | Univariate time-series model | Conditional volatility model |
| Fonte seminale≠ | 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 ↗ |
| Alias≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli |
| Correlati | 5 | 5 |
| Sintesi≠ | 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). | 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. |
| ScholarGateInsieme di dati ↗ |
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