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Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach
Stockholm University, Faculty of Social Sciences, Stockholm Business School, Finance.
2015 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131XArticle in journal (Refereed) In press
Abstract [en]

This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict shortterm and long-term components of the return variance. We investigate several alternative models and use a large group of economic variables. A principal component analysis is used toincor porate the information contained in different variables. Our results show that including low frequency macroeconomic information into the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCHMIDAS 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.

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URN: urn:nbn:se:su:diva-116075OAI: diva2:802195
Available from: 2015-04-11 Created: 2015-04-11 Last updated: 2015-10-29Bibliographically approved

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