방법 비교

	로버스트 ARCH 모형	Robust Regression
분야	계량경제학	통계학
계열	Regression model	Regression model
기원 연도	2002–2008	1964
창시자	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)
유형	Volatility / conditional heteroscedasticity model	Regression with outlier resistance
원전	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.
별칭	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation
관련	6	6
요약	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.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.