Bandingkan metode

	Model ARCH Kuat	Model GARCH (Peramalan Volatilitas)	Regresi Kuantil
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	2002–2008	1986	1978
Pencetus	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s	Tim Bollerslev	Koenker & Bassett
Tipe	Volatility / conditional heteroscedasticity model	Conditional volatility model	Conditional quantile regression
Sumber perintis	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	conditional quantile regression, regression quantiles, Kantil Regresyon
Terkait	6	5	5
Ringkasan	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 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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.