Volatility models with innovations from new maximum entropy densities at work
AbstractGeneralized 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. --
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Bibliographic InfoPaper provided by Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW) in its series IWQW Discussion Paper Series with number 03/2010.
Date of creation: 2010
Date of revision:
GARCH; APARCH; Entropy density; Skewness; Kurtosis;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-04-04 (All new papers)
- NEP-ECM-2010-04-04 (Econometrics)
- NEP-ETS-2010-04-04 (Econometric Time Series)
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