Jämför metoder

	Exponentiell GARCH (EGARCH)	ARIMA (Autoregressive Integrated Moving Average) Modell	Generaliserad Autoregressiv Konditionell Heteroskedasticitet (GARCH)	GJR-GARCH (Asymmetrisk GARCH)	TBATS
Ämnesområde	Ekonometri	Ekonometri	Ekonometri	Ekonometri	Ekonometri
Familj	Regression model	Regression model	Regression model	Regression model	Regression model
Ursprungsår	1991	2015	1986	1993	2011
Upphovsperson	Nelson	Box & Jenkins (Box-Jenkins methodology)	Tim Bollerslev	Glosten, Jagannathan & Runkle (1993); Zakoian (1994)	De Livera, Hyndman & Snyder
Typ	Conditional volatility model (asymmetric GARCH variant)	Univariate time-series model	Conditional volatility model	Asymmetric conditional volatility model	Exponential smoothing state space model
Ursprungskälla	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021	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.	De Livera, A. M., Hyndman, R. J. & Snyder, R. D. (2011). Forecasting Time Series with Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), 1513-1527.
Alias	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	asymmetric GARCH, leverage GARCH, TGARCH, GJR-GARCH — Asimetrik GARCH (Glosten-Jagannathan-Runkle)	trigonometric exponential smoothing, multiple seasonal exponential smoothing, complex seasonal exponential smoothing, TBATS — Çoklu Mevsimsel Üstel Düzleştirme
Närliggande	4	5	5	5	3
Sammanfattning	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).	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).	TBATS is an innovations state space forecasting model, introduced by De Livera, Hyndman and Snyder (2011), that combines a Box-Cox transformation, ARMA errors and trigonometric (Fourier) seasonal terms. It is built to handle continuous time series with several nested seasonal cycles at once — for example hourly data that also repeats daily, weekly and yearly.