Robust maximum entropy test for GARCH models based on a minimum density power divergence estimator

摘要

The maximum entropy test, as designed for examining goodness-of-fit with a non-robust estimator such as the maximum likelihood estimator, can suffer from severe size distortions when the data are contaminated by outliers. The objective of this study is to develop a robust maximum entropy test for the normality of GARCH models. We construct the test statistic based on the minimum density power divergence estimator and verify its limiting null distribution. A bootstrap method is also discussed, and its performance is evaluated through simulations. According to the simulation results, the proposed test can successfully achieve reasonable sizes in the presence of outliers. (C) 2017 Elsevier B.V. All rights reserved.