Comparar métodos

	Modelo GARCH Robusto	Modelo ARCH (Autoregressive Conditional Heteroskedasticity)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1986–2013	1982
Autor original	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Robert F. Engle
Tipo	Volatility model	Conditional volatility model
Fonte seminal	Boudt, K., Danielsson, J., & Laurent, S. (2013). Robust forecasting of dynamic conditional correlation GARCH models. International Journal of Forecasting, 29(2), 244–257.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Outros nomes	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Relacionados	5	6
Resumo	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.	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.