Сравнение на методи

	Robust TGARCH	Модел ARCH (Авторегресивен условен хетероскедастичност)
Област	Иконометрия	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	1994–2000s	1982
Създател	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature	Robert F. Engle
Тип	Volatility model with asymmetry and robust estimation	Conditional volatility model
Основополагащ източник	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Други названия	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Свързани	6	6
Резюме	Robust TGARCH extends the Threshold GARCH model by replacing the conventional maximum likelihood objective with an estimator that is resistant to heavy-tailed innovations and outlying observations. It captures asymmetric volatility responses — where negative shocks amplify variance more than positive shocks — while remaining reliable when the return distribution deviates strongly from normality.	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.