Bandingkan metode

	Fourier EGARCH: Pemodelan Volatilitas dengan Perubahan Struktural yang Mulus	Exponential GARCH (EGARCH)	Generalised Autoregressive Conditional Heteroskedasticity (GARCH)
Bidang	Ekonometrika	Ekonometrika	Ekonometrika
Keluarga	Regression model	Regression model	Regression model
Tahun asal	2010s	1991	1986
Pencetus	Extension of Nelson (1991) EGARCH using Fourier approximation frameworks	Nelson	Tim Bollerslev
Tipe	Volatility model with smooth structural breaks	Conditional volatility model (asymmetric GARCH variant)	Conditional volatility model
Sumber perintis	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.	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
Alias	Fourier-EGARCH, F-EGARCH, Fourier exponential GARCH, smooth structural break EGARCH	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli
Terkait	3	4	5
Ringkasan	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.	EGARCH is an asymmetric GARCH variant, introduced by Nelson in 1991, that models the leverage effect in which bad news raises volatility more than good news of the same size. It captures the negative-shock asymmetry of financial return series by modelling the logarithm of the conditional variance.	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.