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Smoothed empirical likelihood for GARCH models with heavy-tailed errors

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  • Jinyu Li
  • Xingtong Chen
  • Song Zhu

Abstract

This paper proposes an empirical likelihood (EL) method for estimating the GARCH(p, q) models with heavy-tailed errors. Using the kernel smoothing method, we derive a smoothed EL ratio statistic, which yields a smoothed EL estimator. Moreover, we derive a profile EL for the partial parameters in the presence of nuisance parameters. Simulations and empirical results are conducted to illustrate our proposed method.

Suggested Citation

  • Jinyu Li & Xingtong Chen & Song Zhu, 2016. "Smoothed empirical likelihood for GARCH models with heavy-tailed errors," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(24), pages 7275-7293, December.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:24:p:7275-7293
    DOI: 10.1080/03610926.2014.978947
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