ScholarGate
Asistent

Compară metode

Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.

Autoregresivul Condiționat Generalizat cu Heteroscedasticitate (GARCH)×Modelul ARIMA (Autoregresiv Integrat cu Medii Mobile)×GARCH Exponențial (EGARCH)×
DomeniuEconometrieEconometrieEconometrie
FamilieRegression modelRegression modelRegression model
Anul apariției198620151991
Autorul originalTim BollerslevBox & Jenkins (Box-Jenkins methodology)Nelson
TipConditional volatility modelUnivariate time-series modelConditional volatility model (asymmetric GARCH variant)
Sursa seminalăBollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. DOI ↗
Denumiri alternativeGARCH(1,1), generalized ARCH, conditional volatility model, GARCH ModeliBox-Jenkins model, ARIMA(p,d,q), ARIMA Modeliexponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH
Înrudite554
RezumatGARCH 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.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.
ScholarGateSet de date
  1. v1
  2. 1 Surse
  3. PUBLISHED
  1. v1
  2. 1 Surse
  3. PUBLISHED
  1. v1
  2. 2 Surse
  3. PUBLISHED

Mergi la căutare Descarcă prezentarea

ScholarGateCompară metode: GARCH · ARIMA · EGARCH. Preluat la 2026-06-19 de pe https://scholargate.app/ro/compare