Autoregressive Process

An autoregressive process is a type of statistical model where the current value of a variable is based on its own past values. It assumes that the future is partially determined by the past.

In the context of GARCH, it describes how the variance is influenced by previous periods of variance. This structure is useful for modeling trends and momentum in financial data.

It is a building block for many time series models used in economics and finance. By identifying the lag order, analysts can capture the duration of market effects.

It helps in understanding the persistence of shocks in the market. It is a fundamental concept for anyone working with quantitative data.

The model provides a mathematical way to describe how events propagate over time.