Abstract
AbstractThis chapter considers modelling conditional heteroskedasticity and begins with the well known autoregressive conditional heteroskedasticity (ARCH) model. Its basic extension to the generalized autoregressive conditional heteroskedasticity (GARCH) model is described, and various extensions of the GARCH model are considered. They include the exponential GARCH model and the stochastic volatility model that is not a GARCH model but belongs to a separate family of models. Building GARCH models, including specification, estimation and evaluation, is discussed. The GARCH‐in‐mean model and the concept of realized volatility are briefly mentioned. There is also a section on multivariate GARCH models whose popularity has been increasing during the last few years.
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