Usporedite metode

	ARCH model (Autoregressive Conditional Heteroskedasticity)	ARIMA model (Autoregressive Integrated Moving Average)	Model GARCH (Prognoziranje volatilnosti)
Područje	Ekonometrija	Ekonometrija	Ekonometrija
Obitelj	Regression model	Regression model	Regression model
Godina nastanka	1982	1970	1986
Tvorac	Robert F. Engle	George Box and Gwilym Jenkins	Tim Bollerslev
Vrsta	Conditional volatility model	Time series forecasting model	Conditional volatility model
Temeljni izvor	Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.	Box, G. E. P., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. Holden-Day.	Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
Drugi nazivi	ARCH, autoregressive conditional heteroskedasticity, Engle ARCH, conditional variance model	ARIMA, Box-Jenkins model, integrated ARMA, ARIMA(p,d,q)	GARCH, GARCH(1,1), conditional volatility model, GARCH Modeli (Oynaklık Tahmini)
Srodne	6	6	5
Sažetak	The ARCH model, introduced by Robert Engle in 1982, captures time-varying volatility in financial and macroeconomic time series. It models the conditional variance of today's error as a function of past squared errors, explaining why volatile periods cluster together — a phenomenon known as volatility clustering.	The ARIMA(p,d,q) model is the standard workhorse for univariate time series forecasting. It combines autoregressive terms (past values), differencing to induce stationarity, and moving average terms (past shocks) into a unified linear framework. Developed by Box and Jenkins (1970), it remains one of the most widely applied models in econometrics and applied statistics.	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.