Estimating GARCH models: when to use what?

Abstract

The class of generalized autoregressive conditional heteroscedastic (GARCH) models has proved particularly valuable in modelling time series with time varying volatility. These include financial data, which can be particularly heavy tailed. It is well understood now that the tail heaviness of the innovation distribution plays an important role in determining the relative performance of the two competing estimation methods, namely the maximum quasi‐likelihood estimator based on a Gaussian likelihood (GMLE) and the log‐transform‐based least absolutely deviations estimator (LADE) (see Peng and Yao 2003Biometrika,90, 967–75). A practically relevant question is when to use what. We provide in this paper a solution to this question. By interpreting the LADE as a version of the maximum quasilikelihood estimator under the likelihood derived from assuming hypothetically that the log‐squared innovations obey a Laplace distribution, we outline a selection procedure based on some goodness‐of‐fit type statistics. The methods are illustrated with both simulated and real data sets. Although we deal with the estimation for GARCH models only, the basic idea may be applied to address the estimation procedure selection problem in a general regression setting.

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