Methoden vergelijken

	Markov Regime-Switching Model (MS-AR / MS-VAR)	Exponential GARCH (EGARCH)	GARCH (Generalized Autoregressive Conditional Heteroskedasticity)
Vakgebied	Econometrie	Econometrie	Econometrie
Familie	Regression model	Regression model	Regression model
Jaar van ontstaan	1989	1991	1986
Grondlegger	Hamilton (1989); Kim & Nelson (1999)	Nelson	Tim Bollerslev
Type	Regime-switching time series model	Conditional volatility model (asymmetric GARCH variant)	Conditional volatility model
Oorspronkelijke bron	Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
Aliassen	regime-switching model, Markov-switching autoregression, MS-AR, MS-VAR	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli
Verwant	5	4	5
Samenvatting	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.	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.	GARCH 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.