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On the statistical properties of multiplicative GARCH models

We examine the statistical properties of multiplicative GARCH models. First, we show that in multiplicative models, returns have higher kurtosis and squared returns have a more persistent autocorrelation function than in the nested GARCH model. Second, we extend the results of Andersen and Bollersle...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: Conrad, Christian (VerfasserIn) , Kleen, Onno (VerfasserIn)
Dokumenttyp: Book/Monograph Arbeitspapier
Sprache:Englisch
Veröffentlicht: Heidelberg University of Heidelberg, Department of Economics March 18, 2016
Schriftenreihe:Discussion paper series / Universität Heidelberg, Department of Economics No. 613
In: Discussion paper series (no. 613)

DOI:10.11588/heidok.00020486
Schlagworte:
Arbeitspapier
Graue Literatur
Online-Zugang:Resolving-System, kostenfrei, Volltext: http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-204866
Resolving-System, kostenfrei, Volltext: https://doi.org/10.11588/heidok.00020486
Resolving-System, kostenfrei, Volltext: http://hdl.handle.net/10419/162956
Verlag, kostenfrei, Volltext: http://www.ub.uni-heidelberg.de/archiv/20486
Verlag, kostenfrei, Volltext: http://archiv.ub.uni-heidelberg.de/volltextserver/20486/1/conrad_kleen_2016_dp613.pdf
Volltext
Verfasserangaben:Christian Conrad and Onno Kleen
Beschreibung
Zusammenfassung:We examine the statistical properties of multiplicative GARCH models. First, we show that in multiplicative models, returns have higher kurtosis and squared returns have a more persistent autocorrelation function than in the nested GARCH model. Second, we extend the results of Andersen and Bollerslev (1998) on the upper bound of the R2 in a Mincer-Zarnowitz regression to the case of a multiplicative GARCH model, using squared returns as a proxy for the true but unobservable conditional variance. Our theoretical results imply that multiplicative GARCH models provide an explanation for stylized facts that cannot be captured by classical GARCH modeling.
Beschreibung:Online Resource
DOI:10.11588/heidok.00020486

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