Models the time varying volatility with powerful econometric GARCH-Family type of processes and, project future outcomes.
The statistical tools described in this section are designed to model and forecast the conditional variance, or volatility, instead of the conditional mean of a variable. The analysis of the conditional variance may be useful for several reasons, such as pricing an option or improving the estimation of forecast intervals. The models described below assume that the conditional variance in time t depends on past errors and variances. They are designed to model time varying volatility, in particular volatility clustering - a feature often displayed by financial market series. The variance at time t is expected to be higher when past errors and variances were higher in the past and vice verse.
NumXL has a complete set of tools for building on time-varying volatility models. The Add-in supports several variants of univariate GARCH models, including standard ARCH/GARCH models, as well as asymmetric EGARCH and GARCH in the mean (GARCH-M) models designed to capture leverage effects in asset returns
Models with generalized autoregressive conditional heteroskedasticity (GARCH) model
Model conditional variance with exponential general autoregressive conditional heteroskedastic (E-GARCH) model (Nelson 1991).
Model the conditional volatility as a GARCH process, but adds a heteroskedasticity term into the mean equation.