logo

Video encyclopedia

Autoregressive conditional heteroskedasticity

3:52

Basics of ARCH-GARCH Modeling

3:43

DCC GARCH MODEL in Eveiws

2:03

14 Differences of ARCH & GARCH, Econometrics

0:32

Makings model ARCH

2:33

21 Estimating GARCH models

In econometrics, the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods' error terms; often the variance is related to the squares of the previous innovations. The ARCH model is appropriate when the error variance in a time series follows an autoregressive (AR) model; if an autoregressive moving average model (ARMA) model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH) model. For forecasting, combining ARIMA and ARCH models could be considered. For instance, a hybrid ARIMA-ARCH model was examined for shipping freight rate forecast.
    Explore contextually related video stories in a new eye-catching way. Try Combster now!
    • ARCH(''q'') model specification 

    • GARCH