Linganisha mbinu

	Kielelezo cha GARCH kisicho cha mstari	Muundo wa ARCH (Autoregressive Conditional Heteroskedasticity)
Nyanja	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	1991-1993	1982
Mwanzilishi	Glosten, Jagannathan & Runkle; Nelson (1991) for EGARCH	Robert F. Engle
Aina	Volatility model	Conditional volatility model
Chanzo asilia	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.
Majina mbadala	NL-GARCH, asymmetric GARCH, GJR-GARCH, nonlinear volatility model	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model
Zinazohusiana	6	6
Muhtasari	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.