Comparar métodos

	GARCH de Correlação Condicional Dinâmica Robusto (DCC-GARCH Robusto)	Modelo GARCH (Previsão de Volatilidade)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	2002–2021	1986
Autor original	Engle (2002) for DCC; robust extensions by Pakel, Shephard, Sheppard, and Engle (2021)	Tim Bollerslev
Tipo	Multivariate volatility model with robust estimation	Conditional volatility model
Fonte seminal	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.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Outros nomes	robust DCC-GARCH, robust dynamic conditional correlation, outlier-robust DCC, composite-likelihood DCC-GARCH	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Relacionados	6	5
Resumo	The Robust DCC-GARCH model extends Engle's (2002) Dynamic Conditional Correlation framework by replacing standard quasi-maximum likelihood estimation with outlier-resistant or composite-likelihood techniques. This preserves accurate time-varying correlation estimation even when financial return data contain extreme observations, heavy tails, or structural irregularities.	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.