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	Robust ARCH-modell	Autoregressiv modell för betingad heteroskedasticitet (ARCH-modell)	GARCH-modellen (prognostisering av volatilitet)
Ämnesområde	Ekonometri	Ekonometri	Ekonometri
Familj	Regression model	Regression model	Regression model
Ursprungsår	2002–2008	1982	1986
Upphovsperson	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s	Robert F. Engle	Tim Bollerslev
Typ	Volatility / conditional heteroscedasticity model	Conditional volatility model	Conditional volatility model
Ursprungskälla	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Alias	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Närliggande	6	6	5
Sammanfattning	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 ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.	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.