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Autoregressive conditional heteroskedasticity


Basics of ARCH-GARCH Modeling


Time Series Analysis (Georgia Tech) - 4.3.3 - Case Study - ARCH Modelling


BEKK model - Eviews


Estimating Volatilities and Correlations| Financial Risk Manager Exam Questions | FRM Training


Introduction to Arch Finance

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.
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