Comparar métodos

	Modelo ARCH (Autoregressive Conditional Heteroskedasticity)	Modelo EGARCH (GARCH Exponencial)	Modelo TGARCH (GARCH Limiar)
Área	Econometria	Econometria	Econometria
Família	Regression model	Regression model	Regression model
Ano de origem	1982	1991	1993-1994
Autor original	Robert F. Engle	Daniel B. Nelson	Zakoian (1994); Glosten, Jagannathan & Runkle (1993)
Tipo	Conditional volatility model	Volatility / conditional variance model	Asymmetric volatility model
Fonte seminal	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.	Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
Outros nomes	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	Exponential GARCH, EGARCH, Nelson EGARCH, log-GARCH	Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH
Relacionados	6	6	6
Resumo	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.	The Exponential GARCH (EGARCH) model, introduced by Nelson (1991), extends the standard GARCH framework by modelling the logarithm of conditional variance. This ensures variance is always positive without parameter constraints and, crucially, allows negative and positive shocks to have asymmetric effects on volatility — capturing the well-known leverage effect in financial markets.	The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative.