مقایسهٔ روش‌ها

	مدل خودرگرسیون شرطی تعمیم‌یافته ناهمسانی (GARCH)	مدل آریما (میانگین متحرک یکپارچه خودرگرسیو)	دی‌سی‌سی-گارچ (همبستگی شرطی پویا)	مدل نمایی GARCH (EGARCH)
حوزه	اقتصادسنجی	اقتصادسنجی	مالی	اقتصادسنجی
خانواده	Regression model	Regression model	Regression model	Regression model
سال پیدایش	1986	2015	2002	1991
پدیدآور	Tim Bollerslev	Box & Jenkins (Box-Jenkins methodology)	Robert F. Engle	Nelson
نوع	Conditional volatility model	Univariate time-series model	Multivariate volatility model	Conditional volatility model (asymmetric GARCH variant)
منبع بنیادین	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.
نام‌های دیگر	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
مرتبط	5	5	5	4
خلاصه	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.