Comparar métodos

	Heterocedasticidade Condicional Autorregressiva Generalizada (GARCH)	Modelo ARIMA (Autoregressive Integrated Moving Average)	Exponential GARCH (EGARCH)
Área	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Ano de origem	1986	2015	1991
Autor original	Tim Bollerslev	Box & Jenkins (Box-Jenkins methodology)	Nelson
Tipo	Conditional volatility model	Univariate time-series model	Conditional volatility model (asymmetric GARCH variant)
Fonte seminal	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.
Outros nomes	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH
Relacionados	5	5	4
Resumo	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.