방법 비교

	베이지안 EGARCH 모형	ARCH 모형 (자기회귀 조건부 이분산성)
분야	계량경제학	계량경제학
계열	Regression model	Regression model
기원 연도	1991 (EGARCH); 2000s (Bayesian estimation)	1982
창시자	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Robert F. Engle
유형	Volatility model with Bayesian inference	Conditional volatility model
원전	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
별칭	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
관련	6	6
요약	The Bayesian EGARCH model combines Nelson's (1991) Exponential GARCH specification — which models the log of conditional variance and captures the leverage effect — with Bayesian posterior inference via Markov Chain Monte Carlo (MCMC). This allows full uncertainty quantification of all volatility parameters, including the asymmetry coefficient, without requiring large-sample normality of the estimates.	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.