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The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach
Department of Economics, Knut Wicksell Center for Financial Studies, Lund University, Sweden.
Department of Business and Economics, Southern Denmark University, Odense, Denmark.
Department of Statistics, Lund University, Sweden.ORCID iD: 0000-0002-1488-4703
2013 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 32, no 7, p. 600-612Article in journal (Refereed) Published
Abstract [en]

This paper applies the GARCH-MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low-frequency macroeconomic information in the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCH-MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2013. Vol. 32, no 7, p. 600-612
Keywords [en]
Mixed data sampling, long-term variance component, macroeconomic variables, principal component, variance prediction
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:oru:diva-62085DOI: 10.1002/for.2256ISI: 000326065800003Scopus ID: 2-s2.0-84887062530OAI: oai:DiVA.org:oru-62085DiVA, id: diva2:1154023
Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2018-05-29Bibliographically approved

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Javed, Farrukh

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