手法を比較
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| ARIMA（自己回帰和分移動平均）モデル× | 一般化自己回帰条件付き分散 (GARCH)× | |
|---|---|---|
| 分野 | 計量経済学 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2015 | 1986 |
| 提唱者≠ | Box & Jenkins (Box-Jenkins methodology) | Tim Bollerslev |
| 種類≠ | Univariate time-series model | Conditional volatility model |
| 原典≠ | 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 ↗ |
| 別名≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli |
| 関連 | 5 | 5 |
| 概要≠ | 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. |
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