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What is ARCH model and Garch model?

1, ARCH model (Autoregressive conditional heteroskedasticity model) full name "autoregressive conditional heteroskedasticity model", solves the traditional econometrics on the second assumption of the time series variable (variance) constant) of time series variables in traditional econometrics.

2. The GARCH model is called the generalized ARCH model, which is an extension of the ARCH model and was developed by Bollerslev (1986).

(1) The GARCH model (Bollerslev (1986). the GARCH (p, q) model is:

(2) The GARCH-M model introduces the heteroskedasticity term into the mean equation. A simple GARCH-M (1, 1) model can be expressed as:

Extension:

Development of GARCH:

The second assumption of traditional econometrics for time series variables: the assumption that the magnitude of volatility (variance) of a time series variable is fixed is unrealistic; for example, it has long been recognized that the magnitude of volatility of stock returns varies over time and is not constant. This makes traditional time series analysis ineffective for real-world problems.

Robert Engel proposed the ARCH model to address the volatility of time series in a paper published in the journal Econometrica in 1982, when he was studying the volatility of inflation in Britain.

Baidu Encyclopedia-GARCH Model

Baidu Encyclopedia-ARCH Model