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

Byungsoo Kim

Economics Letters, 2018, vol. 162, issue C, 93-97

Abstract: 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.

Keywords: Entropy-based goodness-of-fit test; Normality test; GARCH models; Minimum density power divergence estimator; Parametric bootstrap method (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:162:y:2018:i:c:p:93-97

DOI: 10.1016/j.econlet.2017.11.003

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