Bandingkan kaedah

	Exponential GARCH (EGARCH)	GJR-GARCH (GARCH Asimetri)	Model Peralihan Rejim Markov (MS-AR / MS-VAR)
Bidang	Ekonometrik	Ekonometrik	Ekonometrik
Keluarga	Regression model	Regression model	Regression model
Tahun asal	1991	1993	1989
Pengasas	Nelson	Glosten, Jagannathan & Runkle (1993); Zakoian (1994)	Hamilton (1989); Kim & Nelson (1999)
Jenis	Conditional volatility model (asymmetric GARCH variant)	Asymmetric conditional volatility model	Regime-switching time series model
Sumber perintis	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	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.	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Alias	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR
Berkaitan	4	5	5
Ringkasan	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.	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).	The Markov regime-switching model lets the parameters of a time series change probabilistically across hidden regimes governed by a Markov chain. Introduced by Hamilton (1989) and developed further by Kim and Nelson (1999), it automatically detects business-cycle phases such as expansions and contractions.