방법 비교

	로버스트 ARCH 모형	ARCH 모형 (자기회귀 조건부 이분산성)	EGARCH 모형 (Exponential GARCH)	GARCH 모형 (변동성 예측)	조건부 분위수 회귀
분야	계량경제학	계량경제학	계량경제학	계량경제학	계량경제학
계열	Regression model	Regression model	Regression model	Regression model	Regression model
기원 연도	2002–2008	1982	1991	1986	1978
창시자	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s	Robert F. Engle	Daniel B. Nelson	Tim Bollerslev	Koenker & Bassett
유형	Volatility / conditional heteroscedasticity model	Conditional volatility model	Volatility / conditional variance model	Conditional volatility model	Conditional quantile regression
원전	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.	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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
별칭	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	conditional quantile regression, regression quantiles, Kantil Regresyon
관련	6	6	6	5	5
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.	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 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.	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.