Porównaj metody

	Bayesowski model EGARCH	Model ARCH (Autoregresywna Heteroskedastyczność Warunkowa)
Dziedzina	Ekonometria	Ekonometria
Rodzina	Regression model	Regression model
Rok powstania	1991 (EGARCH); 2000s (Bayesian estimation)	1982
Twórca	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Robert F. Engle
Typ	Volatility model with Bayesian inference	Conditional volatility model
Źródło pierwotne	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.
Inne nazwy	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Pokrewne	6	6
Podsumowanie	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.