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Analyse von Finanzmarktdaten mittels multivariater GARCH-Modelle und Prognose der Volatilität des DAX

Analyse von Finanzmarktdaten mittels multivariater GARCH-Modelle und Prognose der Volatilität des DAX

          
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About the Book

Masterarbeit aus dem Jahr 2019 im Fachbereich BWL - Sonstiges, Note: 1,3, Universität Hamburg, Sprache: Deutsch, Abstract: Ziel dieser Arbeit ist es, die Volatilität des Deutschen Aktienindex (kurz DAX) zu prognostizieren. Es stellt sich die Forschungsfrage, ob kontemporäre Korrelationen im Zuge einer multivariaten Analyse genutzt werden können, um die Qualität der DAX-Volatilitätsprognose zu verbessern. Um diese Frage zu beantworten, werden in den weiterführenden Kapiteln dieser Arbeit univariate und multivariate DAX-Volatilitätsprognosen aufgestellt und anschließend miteinander verglichen. Bei der multivariaten Analyse wird zusätzlich zum DAX der Mid-Cap-DAX (kurz MDAX) in die Modellierung aufgenommen. Da es sich um zwei deutsche Aktienindizes handelt, die ähnlichen volkwirtschaftsspezifischen Einflussfaktoren unterliegen, sind starke kontemporäre Korrelationen zwischen DAX- und MDAX-Renditen zu erwarten. Die Entscheidung, ob sich ein Finanztitel als Anlageobjekt eignet, hängt im Wesentlichen von der Rendite- und der Volatilitätserwartung ab. Die Volatilität beschreibt die Stärke der Wertschwankungen einer Anlage und kann somit als Risikomaß interpretiert werden. Es handelt sich um eine nicht-beobachtbare Größe, die sich durch die (bedingte) Standardabweichung der Renditen approximieren lässt. In den heranführenden Kapiteln dieser Arbeit wird zunächst der grundsätzlichen Fragestellung nachgegangen, ob Rendite- und/oder Volatilitätsverläufe Gesetzmäßigkeiten aufweisen, die mit Hilfe der Zeitreihenanalyse modelliert werden können. Ist dies der Fall, können Prognosen erstellt werden, um die Anlageentscheidung zu optimieren. Zahlreiche Befunde der finanzökonomischen Empirie zeigen, dass hochfrequente Renditeprozesse zwar keine linearen, aber nicht-lineare Abhängigkeiten enthalten. Diese Abhängigkeitsstruktur spiegelt das empirische Erscheinungsbild sich abwechselnder Phasen hoher und niedriger Volatilitäten wider. Das als Volatilitätsclustering bezeichnete Phänomen


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Product Details
  • ISBN-13: 9783346147240
  • Publisher: Grin Verlag
  • Publisher Imprint: Grin Verlag
  • Height: 210 mm
  • No of Pages: 80
  • Spine Width: 5 mm
  • Width: 148 mm
  • ISBN-10: 334614724X
  • Publisher Date: 05 Apr 2020
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Weight: 113 gr


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