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 heteroskedasticity ARCH and generalized ARCH GARCH in the presence of additive outliers AO s We show an alytically that both the asymptotic size and power are adversely a ected if AO s are neglected the test rejects the null hypothesis of homoskedasticity too often when it is in fact true while the test has di culty detecting genuine GARCH e ects Several Monte Carlo experiments show that these phenomena 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 the presence of AO s Applications to the French industrial production series and weekly returns of the Spanish peseta US dollar exchange rate reveal that sometimes apparent GARCH e ects may be due to only a small number of outliers and conversely that genuine GARCH e ects can be masked by outliers