EGARCH models with fat tails, skewness and leverage
Change log
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
1872-7352
Volume Title
76
Publisher
Elsevier BV
Publisher DOI
Sponsorship
Funding from Norges Bank (Norwegian Central Bank) for a research stay in Cambridge is gratefully acknowledged from the second author.