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EGARCH models with fat tails, skewness and leverage

  • Harvey, Andrew
  • Sucarrat, Genaro

An EGARCH model in which the conditional distribution is heavy-tailed and skewed is proposed. The properties of the model, including unconditional moments, autocorrelations and the asymptotic distribution of the maximum likelihood estimator, are set out. Evidence for skewness in a conditional t-distribution is found for a range of returns series, and the model is shown to give a better fit than comparable skewed-t GARCH models in nearly all cases. A two-component model gives further gains in goodness of fit and is able to mimic the long memory pattern displayed in the autocorrelations of the absolute values.

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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 76 (2014)
Issue (Month): C ()
Pages: 320-338

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Handle: RePEc:eee:csdana:v:76:y:2014:i:c:p:320-338
Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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