Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Модел ARIMA (Autoregressive Integrated Moving Average)× | Експоненциален GARCH (EGARCH)× | Модели с дълга памет (ARFIMA, FIGARCH)× | |
|---|---|---|---|
| Област≠ | Иконометрия | Иконометрия | Финанси |
| Семейство | Regression model | Regression model | Regression model |
| Година на възникване≠ | 2015 | 1991 | 1980 |
| Създател≠ | Box & Jenkins (Box-Jenkins methodology) | Nelson | Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH) |
| Тип≠ | Univariate time-series model | Conditional volatility model (asymmetric GARCH variant) | Fractionally integrated time series model |
| Основополагащ източник≠ | 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 ↗ | Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29. DOI ↗ |
| Други названия≠ | Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli | exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH | ARFIMA, FIGARCH, fractionally integrated models, fractional integration |
| Свързани≠ | 5 | 4 | 4 |
| Резюме≠ | 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. | Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration. |
| ScholarGateНабор от данни ↗ |
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