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Unconditional and Conditional Distributional Models for the Nikkei Index

  • Stefan Mittnik
  • Marc Paolella
  • Svetlozar Rachev

We investigate alternative unconditional and conditional distributional models for the returns on Japan's Nikkei 225 stock market index. Among them is the recently introduced class of ARMA-GARCH models driven by α-stable (or stable Paretian) distributed innovations, designed to capture the observed serial dependence, conditional heteroskedasticity and fat-tailedness present in the return data. Of the eight entertained distributions, the partially asymmetric Weibull, Student's t and asymmetric α-stable present themselses as the most viable candidates in terms of overall fit. However, the tails of the sample distribution are approximated best by the asymmetric α-stable distribution. Good tail approximations are particularly important for risk assessments. Copyright Kluwer Academic Publishers 1998

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File URL: http://hdl.handle.net/10.1023/A:1010016831481
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Article provided by Springer in its journal Asia-Pacific Financial Markets.

Volume (Year): 5 (1998)
Issue (Month): 2 (May)
Pages: 99-128

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Handle: RePEc:kap:apfinm:v:5:y:1998:i:2:p:99-128
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