Linganisha mbinu

	Fourier EGARCH: Uundaji wa Volatiliti kwa Mapumziko Laini ya Kimuundo	Umuundo wa Kujirudia kwa Kujitegemea wenye Masharti ya Ugomvi (GARCH)	GJR-GARCH (GARCH Asymmetric)
Nyanja	Ekonometriki	Ekonometriki	Ekonometriki
Familia	Regression model	Regression model	Regression model
Mwaka wa asili	2010s	1986	1993
Mwanzilishi	Extension of Nelson (1991) EGARCH using Fourier approximation frameworks	Tim Bollerslev	Glosten, Jagannathan & Runkle (1993); Zakoian (1994)
Aina	Volatility model with smooth structural breaks	Conditional volatility model	Asymmetric conditional volatility model
Chanzo asilia	Enders, W., & Lee, J. (2012). A unit root test using a Fourier series to approximate smooth breaks. Oxford Bulletin of Economics and Statistics, 74(4), 574-599.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	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. The Journal of Finance, 48(5), 1779-1801.
Majina mbadala	Fourier-EGARCH, F-EGARCH, Fourier exponential GARCH, smooth structural break EGARCH	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)
Zinazohusiana	3	5	5
Muhtasari	Fourier EGARCH extends Nelson's (1991) Exponential GARCH model by embedding Fourier trigonometric terms in the conditional variance equation to capture smooth, gradual shifts in the unconditional variance level over time. This allows the model to handle structural breaks in volatility without requiring prior knowledge of their timing or number.	GARCH is an econometric model for the time-varying volatility of financial time series, introduced by Tim Bollerslev in 1986 as a generalisation of Engle's ARCH model. It treats the conditional variance as a function of past squared shocks and past variances, capturing the volatility clustering seen in returns.	GJR-GARCH is a variant of the GARCH conditional-volatility model that captures the asymmetric effect of negative shocks on volatility using an indicator variable. It was introduced by Glosten, Jagannathan and Runkle (1993), with a closely related threshold formulation by Zakoian (1994).