مقایسهٔ روش‌ها

	مدل‌های حافظه بلندمدت (ARFIMA, FIGARCH)	مدل GARCH (پیش‌بینی نوسانات)
حوزه	مالی	اقتصادسنجی
خانواده	Regression model	Regression model
سال پیدایش	1980	1986
پدیدآور	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)	Tim Bollerslev
نوع	Fractionally integrated time series model	Conditional volatility model
منبع بنیادین	Granger, C. W. J. & Joyeux, R. (1980). An Introduction to Long-Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1(1), 15-29.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
نام‌های دیگر	ARFIMA, FIGARCH, fractionally integrated models, fractional integration	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
مرتبط	4	5
خلاصه	Long-memory models are fractional-integration methods that capture genuine long memory through a hyperbolically decaying autocorrelation structure. ARFIMA, introduced by Granger and Joyeux (1980), models long memory in return series, while FIGARCH, introduced by Baillie, Bollerslev and Mikkelsen (1996), captures long memory in volatility series; the parameter d measures the degree of fractional integration.	The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, introduced by Tim Bollerslev in 1986, models the time-varying conditional variance of a financial time series. It captures volatility clustering and the ARCH effect, and is the standard tool for estimating risk and volatility in return series.