Сравнение методов

	Байесовская модель EGARCH	Модель EGARCH (Экспоненциальная GARCH)
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	1991 (EGARCH); 2000s (Bayesian estimation)	1991
Автор метода	Nelson (1991) for EGARCH; Bayesian inference via MCMC developed from early 2000s	Daniel B. Nelson
Тип	Volatility model with Bayesian inference	Volatility / conditional variance model
Основополагающий источник	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
Другие названия	Bayesian EGARCH model, Bayesian Exponential GARCH, EGARCH with Bayesian estimation, B-EGARCH	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH
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 Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.