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Nelinearni EGARCH model×GARCH model (predviđanje volatilnosti)×
OblastEkonometrijaEkonometrija
PorodicaRegression modelRegression model
Godina nastanka19911986
TvoracDaniel B. NelsonTim Bollerslev
TipConditional volatility modelConditional volatility model
Temeljni izvorNelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. DOI ↗Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. DOI ↗
Drugi naziviNL-EGARCH, nonlinear exponential GARCH, asymmetric EGARCH, NEGARCHGARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Srodne55
SažetakThe Nonlinear EGARCH model extends Nelson's (1991) Exponential GARCH by allowing the news impact function to take a flexible nonlinear form, capturing asymmetric and nonlinear responses of conditional volatility to past shocks. It is widely used in financial econometrics to model leverage effects and complex volatility dynamics in asset returns.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.
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ScholarGateUporedite metode: Nonlinear EGARCH model · GARCH Model. Preuzeto 2026-06-17 sa https://scholargate.app/sr/compare