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Volatility models with innovations from new maximum entropy densities at work

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  • Fischer, Matthias J.
  • Gao, Yang
  • Herrmann, Klaus
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    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. --

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    Bibliographic Info

    Paper 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.

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    Date of creation: 2010
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    Handle: RePEc:zbw:iwqwdp:032010

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    Web page: http://www.iwqw.rw.uni-erlangen.de/
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    Related research

    Keywords: GARCH; APARCH; Entropy density; Skewness; Kurtosis;

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    Cited by:
    1. Schnitzlein, Daniel D., 2011. "How important is cultural background for the level of intergenerational mobility?," Working Papers, University of Aarhus, Aarhus School of Business, Department of Economics 11-12, University of Aarhus, Aarhus School of Business, Department of Economics.
    2. Tinkl, Fabian, 2010. "A note on Hadamard differentiability and differentiability in quadratic mean," IWQW Discussion Paper Series 08/2010, Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW).

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