A stationary unbiased finite sample ARCH-LM test procedure

Abstract

Engle's (1982) ARCH-LM test is the standard test to detect autoregressive conditional heteroscedasticity. In this paper, Monte Carlo simulations are used to demonstrate that the test's statistical size is biased in finite samples. Two complementing remedies to the related problems are proposed. One simple solution is to simulate new unbiased critical values for the ARCH-LM test. A second solution is based on the observation that for econometrics practitioners, detection of ARCH is generally followed by remedial modeling of this time-varying heteroscedasticity by the most general and robust model in the ARCH family; the GARCH(1,1) model. If the GARCH model's stationarity constraints are violated, as in fact is very often the case, obviously, we can conclude that ARCH-LM’s detection of conditional heteroscedasticity has no or limited practical value. Therefore, formulated as a function of whether the GARCH model's stationarity constraints are satisfied or not, an unbiased and more relevant two-step ARCH-LM test is specified. If the primary objectives of the study are to detect and remedy the problems of conditional heteroscedasticity, or to interpret GARCH parameters, the use of this paper’s new two-step procedure, 2S-UARCH-LM, is strongly recommended.