পদ্ধতির তুলনা করুন

	রোবাস্ট TGARCH	শক্তিশালী GARCH মডেল
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1994–2000s	1986–2013
প্রবর্তক	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)
ধরন	Volatility model with asymmetry and robust estimation	Volatility model
মৌলিক উৎস	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.
অপর নাম	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model
সম্পর্কিত	6	5
সারসংক্ষেপ	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 GARCH model extends the classical GARCH framework to handle outliers and heavy-tailed innovations that commonly appear in financial return series. By down-weighting extreme observations through a robust innovation term, it produces more reliable volatility forecasts when data contain jumps, crises, or other anomalies that would otherwise distort standard GARCH estimates.