Сравнение методов

	Экспоненциальный GARCH (EGARCH)	Модель ARIMA (авторегрессионная интегрированная скользящая средняя)	Обобщенная авторегрессионная условная гетероскедастичность (GARCH)
Область	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1991	2015	1986
Автор метода	Nelson	Box & Jenkins (Box-Jenkins methodology)	Tim Bollerslev
Тип	Conditional volatility model (asymmetric GARCH variant)	Univariate time-series model	Conditional volatility model
Основополагающий источник	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	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.
Другие названия	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli
Связанные	4	5	5
Сводка	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.	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.