Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/30185
Authors: 
Fischer, Matthias J.
Gao, Yang
Herrmann, Klaus
Year of Publication: 
2010
Series/Report no.: 
IWQW discussion paper series 03/2010
Abstract: 
Generalized autoregressive conditional heteroskedasticity (GARCH) processes have become very popular as models for financial return data because they are able to capture volatility clustering as well as leptokurtic unconditional distributions which result from the assumption of conditionally normal error distributions. In contrast, Bollerslev (1987) and several follow-ups provided evidence that starting with leptokurtic and possibly skewed (conditional) error distributions will achieve better results. Parallel to these exible but to some extend arbitrary chosen parametric distributions, recent years saw a rise in suggestions for maximum entropy distributions (e.g. Rockinger and Jondeau, 2002, Park and Bera, 2009 or Fischer and Herrmann, 2010). Within this contribution we provide a comprehensive comparison between both different ME densities and their parametric competitors within different generalized GARCH models such as APARCH and GJR-GARCH.
Subjects: 
GARCH
APARCH
Entropy density
Skewness
Kurtosis
Document Type: 
Working Paper

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