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A Cholesky-MIDAS model for predicting stock portfolio volatility

  • Ralf Becker

    (University of Manchester)

  • Adam Clements

    (QUT)

  • Robert O'Neill

    (University of Manchester)

This paper presents a simple forecasting technique for variance covariance matrices. It relies significantly on the contribution of Chiriac and Voev (2010) who propose to forecast elements of the Cholesky decomposition which recombine to form a positive definite forecast for the variance covariance matrix. The method proposed here combines this methodology with advances made in the MIDAS literature to produce a forecasting methodology that is flexible, scales easily with the size of the portfolio and produces superior forecasts in simulation experiments and an empirical application.

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File URL: http://www.ncer.edu.au/papers/documents/WPNo60.pdf
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Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 60.

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Length: 32 pages
Date of creation: 31 Aug 2010
Date of revision:
Handle: RePEc:qut:auncer:2010_07
Contact details of provider: Phone: 07 3138 5066
Fax: 07 3138 1500
Web page: http://www.ncer.edu.au

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  1. Sébastien Laurent & Jeroen Rombouts & Francesco Violente, 2009. "On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models," CIRANO Working Papers 2009s-45, CIRANO.
  2. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  3. Adam Clements & Mark Doolan & Stan Hurn & Ralf Becker, 2009. "Evaluating multivariate volatility forecasts," NCER Working Paper Series 41, National Centre for Econometric Research, revised 25 Nov 2009.
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