Krahasoni metodat

	Modeli jo-linear GARCH	Modeli ARIMA (Autoregresiv i Integruar Mesatar Lëvizës)
Fusha	Ekonometri	Ekonometri
Familja	Regression model	Regression model
Viti i origjinës	1991-1993	1970
Krijuesi	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	George Box and Gwilym Jenkins
Lloji	Volatility model	Time series forecasting model
Burimi themelues	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.
Emërtime të tjera	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)
Të lidhura	6	6
Përmbledhja	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.