Comparar métodos

	Modelos de memoria larga (ARFIMA, FIGARCH)	Modelo GARCH (Predicción de Volatilidad)	Regresión por Mínimos Cuadrados Ordinarios (MCO)
Campo	Finanzas	Econometría	Econometría
Familia	Regression model	Regression model	Regression model
Año de origen	1980	1986	2019
Autor original	Granger & Joyeux (ARFIMA); Baillie, Bollerslev & Mikkelsen (FIGARCH)	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares
Tipo	Fractionally integrated time series model	Conditional volatility model	Linear regression
Fuente seminal	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.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	ARFIMA, FIGARCH, fractionally integrated models, fractional integration	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados	4	5	5
Resumen	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).