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Effects of outliers on the identification and estimation of GARCH models

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URI: https://hdl.handle.net/10016/4742
DOI: 10.1111/j.1467-9892.2006.00519.x
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effects_ruiz_JTSA_2006_ps.pdf (3.55 MB)
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2006-07
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Wiley-Blackwell
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This paper analyses how outliers affect the identification of conditional heteroscedasticity and the estimation of generalized autoregressive conditionally heteroscedastic (GARCH) models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations generated by stationary processes and show that the properties of some conditional homoscedasticity tests can be distorted. Second, we obtain the asymptotic and finite sample biases of the ordinary least squares (OLS) estimator of ARCH(p) models. The finite sample results are extended to generalized least squares (GLS), maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of ARCH(p) and GARCH(1,1) models. Finally, we show that the estimated asymptotic standard deviations are biased estimates of the sample standard deviations.
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Autocorrelations, Generalized least squares heteroscedasticity, Maximum likelihood, McLeod, Li test, Ordinary least squares
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Journal of Time Series Analysis, 2007, vol. 28, n. 4, p. 471-497
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