Porovnat metody

	Bayesovský model EGARCH	Model ARCH (Autoregresivní podmíněná heteroskedasticita)
Obor	Ekonometrie	Ekonometrie
Rodina	Regression model	Regression model
Rok vzniku	1991 (EGARCH); 2000s (Bayesian estimation)	1982
Tvůrce	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Robert F. Engle
Typ	Volatility model with Bayesian inference	Conditional volatility model
Původní zdroj	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.
Další názvy	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Příbuzné	6	6
Shrnutí	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.