Comparar métodos

	Modelo GARCH Não Linear	Modelo ARCH (Autoregressive Conditional Heteroskedasticity)
Área	Econometria	Econometria
Família	Regression model	Regression model
Ano de origem	1991-1993	1982
Autor original	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	Robert F. Engle
Tipo	Volatility model	Conditional volatility model
Fonte seminal	Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779-1801.	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
Outros nomes	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Relacionados	6	6
Resumo	The Nonlinear GARCH model extends the standard GARCH framework to capture asymmetric and nonlinear responses of conditional volatility to past shocks. It allows negative returns (bad news) to amplify volatility more than positive returns of equal magnitude, a phenomenon known as the leverage effect, which is empirically pervasive in financial markets.	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.