Comparar métodos

	Modelo GARCH Robusto	Modelo ARCH (Heterocedasticidad Autoregresiva Condicional)	Regresión Cuantílica
Campo	Econometría	Econometría	Econometría
Familia	Regression model	Regression model	Regression model
Año de origen	1986–2013	1982	1978
Autor original	Boudt, Danielsson & Laurent (robust extensions); Bollerslev (standard GARCH, 1986)	Robert F. Engle	Koenker & Bassett
Tipo	Volatility model	Conditional volatility model	Conditional quantile regression
Fuente 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.	Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50.
Alias	Robust GARCH, outlier-robust GARCH, heavy-tail GARCH, contamination-robust volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	conditional quantile regression, regression quantiles, Kantil Regresyon
Relacionados	5	6	5
Resumen	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.	Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails.