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No . 2001-1 ACCURACY OF MODEL SELECTION CRITERIA FOR A CLASS OF AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTIC MODELS

Many empirical studies relying on standard model selection criteria (e.g., Akaike information criterion (AIC) and Schwarz Bayesian information criterion (BIC)) favourably choose the Generalized autoregressive conditional heteroscedastic GARCH(1,1) model for characterizing financial market volatility. This paper offers some insight into the accuracy of selecting a GARCH(1,1) model when the true model is an autoregressive conditional heteroscedastic (ARCH) model. Moreover, Grose and King (1993) found that a selection criterion for autoregressive moving-average (ARMA) models can biasedly select a particular model due to the shape of the likelihood function. Monte Carlo simulations are used to illustrate the unbiased selection of the GARCH(1,1) model. Our study suggests that GARCH(1,1) models are not unduly favoured when ARCH models are the true models.