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Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach

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Author Info

  • Asgharian, Hossein

    ()

  • Hou, Ai Jun
  • Javed, Farrukh

Abstract

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.

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Bibliographic Info

Paper provided by Knut Wicksell Centre for Financial Studies, Lund University in its series Knut Wicksell Working Paper Series with number 2013/4.

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Length: 30 pages
Date of creation: 24 Feb 2013
Date of revision:
Handle: RePEc:hhs:luwick:2013_004

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Postal: Knut Wicksell Centre for Financial Studies, Lund University School of Economics and Management, P.O. Box 7080, S-220 07 Lund, Sweden
Phone: +46 46-222 32 61
Fax: +46 46-222 34 06
Web page: http://www.lusem.lu.se/kwc
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Related research

Keywords: Mixed data sampling; long-term variance component; macroeconomic variables; principal component; variance prediction.;

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References

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  1. G. William Schwert, 1990. "Why Does Stock Market Volatility Change Over Time?," NBER Working Papers 2798, National Bureau of Economic Research, Inc.
  2. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
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  14. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
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Cited by:
  1. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC-IPTS Working Papers, Institute for Prospective and Technological Studies, Joint Research Centre JRC84138, Institute for Prospective and Technological Studies, Joint Research Centre.
  2. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2014. "Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification," CREATES Research Papers 2014-13, School of Economics and Management, University of Aarhus.

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