مقایسهٔ روش‌ها

	TGARCH ساختاری (TGARCH آستانه‌ای با شکست‌های ساختاری)	مدل EGARCH (نمایی GARCH)	مدل GARCH (پیش‌بینی نوسانات)
حوزه	اقتصادسنجی	اقتصادسنجی	اقتصادسنجی
خانواده	Regression model	Regression model	Regression model
سال پیدایش	1990-1993	1991	1986
پدیدآور	Lamoureux & Lastrapes (structural breaks in GARCH); Glosten, Jagannathan & Runkle (TGARCH/GJR-GARCH asymmetry)	Daniel B. Nelson	Tim Bollerslev
نوع	Volatility model	Volatility / conditional variance model	Conditional volatility model
منبع بنیادین	Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business & Economic Statistics, 8(2), 225-234.	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.
نام‌های دیگر	SB-TGARCH, threshold GARCH with structural breaks, GJR-GARCH with structural breaks, break-adjusted TGARCH	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
مرتبط	3	6	5
خلاصه	Structural Break TGARCH extends the Threshold GARCH (GJR-GARCH) model to accommodate discrete, permanent shifts in the volatility process. By detecting structural breaks and incorporating them — either as regime-specific intercepts or dummy variables — the model separates genuine volatility persistence from spurious persistence induced by ignored regime changes, and preserves the asymmetric leverage effect that characterises equity and financial return data.	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.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.