Fat tails and asymmetry in financial volatility models

Peter Verhoeven and Michael McAleer

Mathematics and Computers in Simulation (MATCOM), 2004, vol. 64, issue 3, 351-361

Abstract: Although the generalised autoregressive conditional heteroskedasticity (GARCH) model has been quite successful in capturing important empirical aspects of financial data, particularly for the symmetric effects of volatility, it has had far less success in capturing the effects of extreme observations, outliers and skewness in returns. This paper examines the GARCH model under various non-normal error distributions in order to evaluate skewness and leptokurtosis. The empirical results show that GARCH models estimated using asymmetric leptokurtic distributions are superior to their counterparts estimated under normality, in terms of: (i) capturing skewness and leptokurtosis; (ii) the maximized log-likelihood values; and (iii) isolating the ARCH and GARCH parameter estimates from the adverse effects of outliers. Overall, the flexible asymmetric Student’s t-distribution performs best in capturing the non-normal aspects of the data.

Keywords: Asymmetric volatility; Conditional non-normality; Skewness; Leptokurtosis; Outliers; Location parameter (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (17)

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Working Paper: Fat Tails and Asymmetry in Financial Volatility Models (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:64:y:2004:i:3:p:351-361

DOI: 10.1016/S0378-4754(03)00101-0

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