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Uogólniona warunkowa heteroskedastyczność z autoregresją (GARCH)×GJR-GARCH (Asymetryczny GARCH)×
DziedzinaEkonometriaEkonometria
RodzinaRegression modelRegression model
Rok powstania19861993
TwórcaTim BollerslevGlosten, Jagannathan & Runkle (1993); Zakoian (1994)
TypConditional volatility modelAsymmetric conditional volatility model
Źródło pierwotneBollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327. DOI ↗Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801. DOI ↗
Inne nazwyGARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeliasymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)
Pokrewne55
PodsumowanieGARCH 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.GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994).
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ScholarGatePorównaj metody: GARCH · GJR-GARCH. Pobrano 2026-06-18 z https://scholargate.app/pl/compare