قارن الطرق

	نموذج GARCH غير الخطي	نموذج ARIMA (الانحدار الذاتي المتكامل المتوسط المتحرك)
المجال	الاقتصاد القياسي	الاقتصاد القياسي
العائلة	Regression model	Regression model
سنة النشأة	1991-1993	1970
صاحب الطريقة	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	George Box and Gwilym Jenkins
النوع	Volatility model	Time series forecasting model
المصدر التأسيسي	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.
الأسماء البديلة	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
ذات صلة	6	6
الملخص	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.