Salīdzināt metodes

	Robust TGARCH	DCC-GARCH modelis (Dynamic Conditional Correlation)
Nozare	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1994–2000s	2002
Autors	Zakoian (1994) for TGARCH; robust extensions developed through quasi-maximum likelihood and M-estimation literature	Robert F. Engle
Tips	Volatility model with asymmetry and robust estimation	Multivariate volatility model
Pirmavots	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.	Engle, R. F. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20(3), 339-350.
Citi nosaukumi	robust GJR-GARCH, robust threshold GARCH, heavy-tail TGARCH, outlier-robust TGARCH	DCC-GARCH, Dynamic Conditional Correlation GARCH, Engle DCC model, multivariate DCC
Saistītās	6	5
Kopsavilkums	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 DCC-GARCH model, introduced by Engle (2002), extends univariate GARCH to capture time-varying correlations between multiple financial time series. It decomposes the multivariate conditional covariance matrix into individual volatility processes and a dynamic correlation matrix, allowing correlations to fluctuate over time while remaining computationally tractable even with many series.