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Implementing likelihood-based inference for fat-tailed distributions

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  • Rekkas, M.
  • Wong, A.

Abstract

The theoretical advancements in higher-order likelihood-based inference methods have been tremendous over the past two decades. The application of these methods in the applied literature however has been far from widespread. A critical barrier to adoption has likely been the computational difficulties associated with the implementation of these methods. This paper provides the applied researcher with a systematic exposition of the calculations and computer code required to implement the higher-order conditional inference methodology of Fraser and Reid [1995. Utilitas Mathematica 47, 33-53] for problems involving heavy- or fat-tailed distributions.

Suggested Citation

  • Rekkas, M. & Wong, A., 2008. "Implementing likelihood-based inference for fat-tailed distributions," Finance Research Letters, Elsevier, vol. 5(1), pages 32-46, March.
    • Handle: RePEc:eee:finlet:v:5:y:2008:i:1:p:32-46
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    Cited by:

    1. Preminger, Arie & Storti, Giuseppe, 2014. "Least squares estimation for GARCH (1,1) model with heavy tailed errors," MPRA Paper 59082, University Library of Munich, Germany.

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