Sammenlign metoder

	Robust ARCH-model	EGARCH-model (Eksponentiel GARCH)	GARCH-model (volatilitetsprognoser)
Fagområde	Økonometri	Økonometri	Økonometri
Familie	Regression model	Regression model	Regression model
Oprindelsesår	2002–2008	1991	1986
Ophavsperson	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s	Daniel B. Nelson	Tim Bollerslev
Type	Volatility / conditional heteroscedasticity model	Volatility / conditional variance model	Conditional volatility model
Oprindelig kilde	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	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.
Aliasser	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Relaterede	6	6	5
Resumé	The Robust ARCH model extends the classical Autoregressive Conditional Heteroscedasticity framework by replacing the standard maximum-likelihood estimator with robust alternatives that downweight or eliminate the influence of outliers. This makes volatility estimates resistant to extreme observations that frequently contaminate financial and macroeconomic time series.	The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.	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.