Bandingkan metode

	Robust TGARCH	Model ARCH Kuat
Bidang	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model
Tahun asal	1994–2000s	2002–2008
Pencetus	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature	Engle (1982) for ARCH; robust variants developed by Muler, Yohai, and others from the early 2000s
Tipe	Volatility model with asymmetry and robust estimation	Volatility / conditional heteroscedasticity model
Sumber perintis	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.
Alias	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH	robust ARCH, outlier-robust ARCH, heavy-tailed ARCH, robust conditional volatility model
Terkait	6	6
Ringkasan	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 Robust ARCH model extends the classical Autoregressive Conditional Heteroscedasticity framework by replacing the standard maximum-likelihood estimator with robust alternatives that downweight or eliminate the influence of outliers. This makes volatility estimates resistant to extreme observations that frequently contaminate financial and macroeconomic time series.