Autoregressive Conditional Heteroskedasticity
The Autoregressive Conditional Heteroskedasticity (ARCH) is a basic empirical model to capture volatility dynamics when analyzing financial markets.
Autoregressive Conditional Heteroskedasticity (ARCH)
The Autoregressive Conditional Heteroskedasticity (ARCH) is a basic empirical model to capture volatility dynamics when analyzing financial markets. ARCH is used whenever there's reason to believe that the variance of the current error term is a function of the actual sizes of the previous time periods' error terms.
AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY APPLICATION
ARCH models are employed commonly in modeling financial time series that exhibit time-varying volatility clustering, i.e. periods of swings followed by periods of relative calm. Although the ARCH model is the basic specification when investigating on conditional volatility, there are some problems that make the model difficult to apply in practice.
The issue of using Maximum Likelihood or an alternative method to estimate the model is not very significant anymore, as current software packages handle such estimations easily. The more relevant problem is that it is difficult to determine the exact number of lags and a quite large number is often required.
As a result, more advanced models are often applied that are based on the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) approach.