ARCH Effect

The ARCH effect refers to the presence of autoregressive conditional heteroskedasticity in a time series. This means that the variance of the current error term is related to the squared values of past error terms.

It is the fundamental observation that necessitates the use of GARCH or ARCH models. When the ARCH effect is present, the market is demonstrating volatility persistence.

Traders use this to determine if they need to adjust their risk models for changing volatility environments. The effect is common in high-frequency trading data and cryptocurrency markets.

If a model ignores the ARCH effect, it will likely underestimate risk during turbulent periods. Identifying this effect is the first step in building a sophisticated volatility forecasting engine.

It provides empirical evidence that market risk is dynamic.