Сравнение методов

	Экспоненциальный GARCH (EGARCH)	Обобщенная авторегрессионная условная гетероскедастичность (GARCH)	Регрессия методом обыкновенных наименьших квадратов (ОНМК)
Область	Эконометрика	Эконометрика	Эконометрика
Семейство	Regression model	Regression model	Regression model
Год появления	1991	1986	2019
Автор метода	Nelson	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
Тип	Conditional volatility model (asymmetric GARCH variant)	Conditional volatility model	Linear regression
Основополагающий источник	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Другие названия	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Связанные	4	5	5
Сводка	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).