Testing for ARCH in the presence of additive outliers
研究了当数据中存在加性异常值时，拉格朗日乘子检验对自回归条件异方差（ARCH）和广义ARCH（GARCH）效应的检验性质，发现异常值会导致检验过度拒绝同方差假设或难以检测真实GARCH效应，并提出了一个稳健检验方法。
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 aected 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 diculty detecting genuine GARCH eects. 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 eects may be due to only a small number of outliers and, conversely, that genuine GARCH eects can be masked by outliers.