Compară metode

	Model GARCH neliniar	Model ARIMA (Autoregresiv Integrat Medie Mobilă)
Domeniu	Econometrie	Econometrie
Familie	Regression model	Regression model
Anul apariției	1991-1993	1970
Autorul original	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	George Box and Gwilym Jenkins
Tip	Volatility model	Time series forecasting model
Sursa seminală	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.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.
Denumiri alternative	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Înrudite	6	6
Rezumat	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 ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.