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EGARCH models with fat tails, skewness and leverage


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Authors

Harvey, A 
Sucarrat, G 

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 tt-distribution is found for a range of returns series, and the model is shown to give a better fit than comparable skewed-tt 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.

Description

Keywords

General error distribution, Heteroskedasticity, Leverage, Score, Student's t, Two components, Volatility

Journal Title

Computational Statistics and Data Analysis

Conference Name

Journal ISSN

0167-9473
1872-7352

Volume Title

76

Publisher

Elsevier BV
Sponsorship
Funding from Norges Bank (Norwegian Central Bank) for a research stay in Cambridge is gratefully acknowledged from the second author.