方法对比

	贝叶斯自回归条件异方差模型	贝叶斯 EGARCH 模型
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	1982 (ARCH); 1989 (Bayesian estimation)	1991 (EGARCH); 2000s (Bayesian estimation)
提出者	Robert F. Engle (ARCH, 1982); Bayesian treatment: John Geweke (1989)	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s
类型	Volatility model with Bayesian inference	Volatility model with Bayesian inference
开创性文献	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.
别名	Bayesian ARCH, ARCH with Bayesian estimation, Bayesian conditional heteroskedasticity model, B-ARCH	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH
相关	6	6
摘要	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.