Сравнение методов

	Байесовская модель EGARCH	Модель ARCH (авторегрессионная условная гетероскедастичность)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1991 (EGARCH); 2000s (Bayesian estimation)	1982
Автор метода	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Robert F. Engle
Тип	Volatility model with Bayesian inference	Conditional volatility model
Основополагающий источник	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.
Другие названия	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Связанные	6	6
Сводка	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.