skip to content

Faculty of Economics

Journal Cover

Harvey, A. C. and Sucarrat, G.

EGARCH models with fat tails, skewness and leverage

Computational Statistics & Data Analysis

Vol. 76 pp. 320-338 (2014)

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.

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

JEL Codes: C22, G17

Author links: Andrew Harvey  

Publisher's Link:

Keynes Fund Project(s): Dynamic Models for volatility and heavy tails  Dynamic Models for volatility and heavy tails  

Cambridge Working Paper in Economics Version of Paper: EGARCH models with fat tails, skewness and leverage, Harvey, A. C. and Sucarrat, G., (2012)

Papers and Publications

Recent Publications

Fruehwirth, J., Iyer, S. and Zhang, A. Religion and Depression in Adolescence Journal of Political Economy [2019]

Carvalho, V. M. and Grassi, B. Large Firm Dynamics and the Business Cycle American Economic Review [2019]

Li, Z. M., Laeven, R. J. A. and Vellekoop, M. H. Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency Data Journal of Econometrics [2020]

Liu, K. Wage Risk and the Value of Job Mobility in Early Employment Careers Journal of Labor Economics [2019]