ScholarGate
Ассистент

Сравнение методов

Просматривайте выбранные методы рядом; строки с различиями подсвечены.

Обобщенная авторегрессионная условная гетероскедастичность (GARCH)×Экспоненциальный GARCH (EGARCH)×Простое и двойное экспоненциальное сглаживание (SES / Холт)×
ОбластьЭконометрикаЭконометрикаЭконометрика
СемействоRegression modelRegression modelRegression model
Год появления198619911957
Автор методаTim BollerslevNelsonRobert G. Brown (SES); Charles C. Holt (linear trend)
ТипConditional volatility modelConditional volatility model (asymmetric GARCH variant)Exponential smoothing forecasting model
Основополагающий источникBollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill. link ↗
Другие названияGARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeliexponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCHSES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)
Связанные543
СводкаGARCH 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.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.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.
ScholarGateНабор данных
  1. v1
  2. 1 Источники
  3. PUBLISHED
  1. v1
  2. 2 Источники
  3. PUBLISHED
  1. v1
  2. 2 Источники
  3. PUBLISHED

Перейти к поиску Скачать слайды

ScholarGateСравнение методов: GARCH · EGARCH · Exponential Smoothing. Получено 2026-06-19 из https://scholargate.app/ru/compare