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EGARCH models with fat tails, skewness and leverage

Andrew Harvey and Genaro Sucarrat

Computational Statistics & Data Analysis, 2014, vol. 76, issue C, 320-338

Abstract: 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.

Keywords: General error distribution; Heteroskedasticity; Leverage; Score; Student’s t; Two components; Volatility (search for similar items in EconPapers)
Date: 2014
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Working Paper: EGARCH models with fat tails, skewness and leverage (2012) Downloads
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