Fat Tails and Asymmetry in Financial Volatility Models
AbstractAlthough the 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-t distribution performs best in terms of capturing the non-normal aspects of the data.
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Bibliographic InfoPaper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-211.
Length: 18 pages
Date of creation: Mar 2003
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-04-02 (All new papers)
- NEP-ECM-2003-04-04 (Econometrics)
- NEP-ETS-2003-04-02 (Econometric Time Series)
- NEP-FIN-2003-04-02 (Finance)
- NEP-FMK-2003-04-02 (Financial Markets)
- NEP-RMG-2003-04-02 (Risk Management)
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- Yuichi Nagahara, 2011. "Using Nonnormal Distributions to Analyze the Relationship Between Stock Returns in Japan and the US," Asia-Pacific Financial Markets, Springer, vol. 18(4), pages 429-443, November.
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- Amélie Charles, 2008. "Forecasting volatility with outliers in GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 551-565.
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