Testing for ARCH in the presence of additive outliers

In this paper we investigate the properties of the Lagrange Multiplier [LM] test for autoregressive conditional heteroscedasticity (ARCH) and generali zed ARCH (GARCH) in the presence of additive outliers (AOs). We show analyt ically that both the asymptotic size and power are adversely affected if AO s are neglected: the test rejects the null hypothesis of homoscedasticity t oo often when it is in fact true, while the test has difficulty detecting g enuine GARCH effects. Several Monte Carlo experiments show that these pheno mena occur in small samples as well. We design and implement a robust test, which has better size and power properties than the conventional test in t he presence of AOs. We apply the tests to a number of US macroeconomic time series, which illustrates the dangers involved when nonrobust tests for AR CH are routinely applied as diagnostic tests for misspecification. Copyrigh t (C) 1999 John Wiley & Sons, Ltd.