Compară metode

	Modelul Bayesian ARCH	Modelul EGARCH Bayesian
Domeniu	Econometrie	Econometrie
Familie	Regression model	Regression model
Anul apariției	1982 (ARCH); 1989 (Bayesian estimation)	1991 (EGARCH); 2000s (Bayesian estimation)
Autorul original	Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989)	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s
Tip	Volatility model with Bayesian inference	Volatility model with Bayesian inference
Sursa seminală	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.
Denumiri alternative	Bayesian ARCH, ARCH with Bayesian estimation, Bayesian conditional heteroskedasticity model, B-ARCH	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH
Înrudite	6	6
Rezumat	The Bayesian ARCH model estimates Engle's Autoregressive Conditional Heteroskedasticity specification within a Bayesian framework. Instead of maximising a likelihood, it combines a prior distribution over the volatility parameters with the data likelihood to obtain a full posterior distribution, providing richer uncertainty quantification than classical maximum-likelihood ARCH.	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.