Arbeitspapier

Volatility models with innovations from new maximum entropy densities at work

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.

Language
Englisch

Bibliographic citation
Series: IWQW Discussion Papers ; No. 03/2010

Classification
Wirtschaft
Subject
GARCH
APARCH
Entropy density
Skewness
Kurtosis
Börsenkurs
Volatilität
ARCH-Modell
Entropie
Statistische Verteilung
Theorie

Event
Geistige Schöpfung
(who)
Fischer, Matthias J.
Gao, Yang
Herrmann, Klaus
Event
Veröffentlichung
(who)
Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW)
(where)
Nürnberg
(when)
2010

Handle
Last update
10.03.2025, 11:44 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Fischer, Matthias J.
  • Gao, Yang
  • Herrmann, Klaus
  • Friedrich-Alexander-Universität Erlangen-Nürnberg, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung (IWQW)

Time of origin

  • 2010

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