Comparar métodos

	Generalización Autorregresiva Condicionalmente Heterocedástica (GARCH)	Regresión por Mínimos Cuadrados Ordinarios (MCO)	ARIMA estacional (SARIMA)
Campo	Econometría	Econometría	Econometría
Familia	Regression model	Regression model	Regression model
Año de origen	1986	2019	2015
Autor original	Tim Bollerslev	Wooldridge (textbook treatment); classical least squares	Box & Jenkins (seasonal extension of ARIMA)
Tipo	Conditional volatility model	Linear regression	Seasonal time-series model
Fuente seminal	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	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
Alias	GARCH(1,1), generalized ARCH, conditional volatility model, GARCH Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	seasonal ARIMA, Box-Jenkins seasonal model, SARIMA — Mevsimsel ARIMA
Relacionados	5	5	5
Resumen	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.	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).	SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.