方法对比

	贝叶斯GARCH模型	自回归条件异方差 (ARCH) 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	1989–2000	1982
提出者	Geweke (1989); further developed by Nakatsuma (2000) and Bauwens & Lubrano (1998)	Robert F. Engle
类型	Bayesian volatility model	Conditional volatility model
开创性文献	Geweke, J. (1989). Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics, 40(1), 63–86.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
别名	Bayesian GARCH, BGARCH, GARCH with Bayesian inference, Bayesian volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
相关	4	6
摘要	The Bayesian GARCH model combines the GARCH framework for time-varying volatility with Bayesian posterior inference. Instead of maximising a likelihood, it specifies prior distributions for the GARCH parameters and draws from the resulting posterior — typically via Markov chain Monte Carlo (MCMC) — to quantify both point estimates and full uncertainty about volatility dynamics.	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.