Сравнение методов

	Модель GARCH (прогнозирование волатильности)	Простое и двойное экспоненциальное сглаживание (SES / Холт)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1986	1957
Автор метода	Tim Bollerslev	Robert G. Brown (SES); Charles C. Holt (linear trend)
Тип	Conditional volatility model	Exponential smoothing forecasting model
Основополагающий источник	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.
Другие названия	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)
Связанные	5	3
Сводка	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.