The GARCH model may perform better in cases where theory suggests that the data generating process produces true autoregressive conditional heteroscedasticity.

The generalized autoregressive conditional heteroskedasticity (GARCH) process is an econometric term used to describe an approach to estimate volatility in financial markets.

In addition, you can consider the model with disturbances following an autoregressive process and with the GARCH errors. The AR(m)-GARCH(p,q) regression model is denoted Nelson and Cao (1992) proposed ...

David E. Rapach, Jack K. Strauss, Structural Breaks and Garch Models of Exchange Rate Volatility, Journal of Applied Econometrics, Vol. 23, No. 1, Themes in Financial ...

This article examines the persistence of the variance, as measured by the generalized auto-regressive conditional heteroskedasticity (GARCH) model, in stock-return data. In particular, we investigate ...

Autoregressive conditional heteroskedasticity is a time-series statistical model used to analyze volatility in high frequency data.