Salīdzināt metodes

	Generalizētā autoregresīvā nosacītā heteroskedastiskuma (GARCH) modelis	ARIMA (autoregresīvais integrētais slīdošā vidējā) modelis	DCC-GARCH (dinamiskā nosacītā korelācija)	EGARCH (Exponential GARCH)	Vienkāršā un dubultā eksponenciālā izlīdzināšana (SES / Holt)
Nozare	Ekonometrija	Ekonometrija	Finanses	Ekonometrija	Ekonometrija
Saime	Regression model	Regression model	Regression model	Regression model	Regression model
Izcelsmes gads	1986	2015	2002	1991	1957
Autors	Tim Bollerslev	Box & Jenkins (Box-Jenkins methodology)	Robert F. Engle	Nelson	Robert G. Brown (SES); Charles C. Holt (linear trend)
Tips	Conditional volatility model	Univariate time-series model	Multivariate volatility model	Conditional volatility model (asymmetric GARCH variant)	Exponential smoothing forecasting model
Pirmavots	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307-327.	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	Engle, R. (2002). Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models. Journal of Business & Economic Statistics, 20(3), 339-350.	Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.	Brown, R. G. (1959). Statistical Forecasting for Inventory Control. McGraw-Hill.
Citi nosaukumi	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	dynamic conditional correlation, Engle DCC, multivariate GARCH, DCC-GARCH — Dinamik Koşullu Korelasyon	exponential GARCH, Nelson's EGARCH, asymmetric GARCH, EGARCH — Üstel GARCH	SES, Holt's linear trend method, exponential smoothing forecasting, Basit ve Çift Üstel Düzleştirme (SES / Holt)
Saistītās	5	5	5	4	3
Kopsavilkums	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.	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).	DCC-GARCH is Engle's (2002) multivariate volatility model that lets the correlations between several assets change over time. A separate univariate GARCH model is fitted to each series, and then the dynamic correlation matrix is estimated in a second, separate step.	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.	Exponential smoothing is a family of basic time-series forecasting models in which each new observation updates a smoothed estimate by a weighting parameter. Simple exponential smoothing (SES), introduced by Robert G. Brown in 1959, forecasts series with a stable level, while Holt's double exponential smoothing, introduced by Charles C. Holt in 1957, adds a trend term using the parameters alpha and beta.